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1. Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation

Author

Sadikine, Amine, Badic, Bogdan, Tasu, Jean-Pierre, Noblet, Vincent, Visvikis, Dimitris, and Conze, Pierre-Henri

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy or surgical planning. Despite a good ability to extract large anatomical structures, the capacity of U-Net inspired architectures to automatically delineate vascular systems remains a major issue, especially given the scarcity of existing datasets. In this paper, we present a novel approach that integrates into deep segmentation shape priors from a Semi-Overcomplete Convolutional Auto-Encoder (S-OCAE) embedding. Compared to standard Convolutional Auto-Encoders (CAE), it exploits an over-complete branch that projects data onto higher dimensions to better characterize tiny structures. Experiments on retinal and liver vessel extraction, respectively performed on publicly-available DRIVE and 3D-IRCADb datasets, highlight the effectiveness of our method compared to U-Net trained without and with shape priors from a traditional CAE., Comment: 5 pages, 4 figures, conference

Published
2024
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2. Deep vessel segmentation with joint multi-prior encoding

Author

Sadikine, Amine, Badic, Bogdan, Ferrante, Enzo, Noblet, Vincent, Ballet, Pascal, Visvikis, Dimitris, and Conze, Pierre-Henri

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The precise delineation of blood vessels in medical images is critical for many clinical applications, including pathology detection and surgical planning. However, fully-automated vascular segmentation is challenging because of the variability in shape, size, and topology. Manual segmentation remains the gold standard but is time-consuming, subjective, and impractical for large-scale studies. Hence, there is a need for automatic and reliable segmentation methods that can accurately detect blood vessels from medical images. The integration of shape and topological priors into vessel segmentation models has been shown to improve segmentation accuracy by offering contextual information about the shape of the blood vessels and their spatial relationships within the vascular tree. To further improve anatomical consistency, we propose a new joint prior encoding mechanism which incorporates both shape and topology in a single latent space. The effectiveness of our method is demonstrated on the publicly available 3D-IRCADb dataset. More globally, the proposed approach holds promise in overcoming the challenges associated with automatic vessel delineation and has the potential to advance the field of deep priors encoding., Comment: 5 pages, 3 figures, conference

Published
2024
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3. Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation

Author

Sadikine, Amine, Badic, Bogdan, Tasu, Jean-Pierre, Noblet, Vincent, Ballet, Pascal, Visvikis, Dimitris, and Conze, Pierre-Henri

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this respect, an automated liver blood vessel extraction is widely summoned. Despite the significant growth in performance of semantic segmentation methodologies, preserving the complex multi-scale geometry of main vessels and ramifications remains a major challenge. This paper provides a new deep supervised approach for vessel segmentation, with a strong focus on representations arising from the different scales inherent to the vascular tree geometry. In particular, we propose a new clustering technique to decompose the tree into various scale levels, from tiny to large vessels. Then, we extend standard 3D UNet to multi-task learning by incorporating scale-specific auxiliary tasks and contrastive learning to encourage the discrimination between scales in the shared representation. Promising results, depicted in several evaluation metrics, are revealed on the public 3D-IRCADb dataset., Comment: 5 pages, 5 figures, conference

Published
2024
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4. Direct3{\gamma}: A Pipeline for Direct Three-gamma PET Image Reconstruction

Author

Mellak, Youness, Bousse, Alexandre, Merlin, Thibaut, Giovagnoli, Debora, and Visvikis, Dimitris

Subjects
Physics - Medical Physics
Abstract

This paper presents a novel image reconstruction pipeline for three-gamma (3-{\gamma}) positron emission tomography (PET) aimed at improving spatial resolution and reducing noise in nuclear medicine. The proposed Direct3{\gamma} pipeline addresses the inherent challenges in 3-{\gamma} PET systems, such as detector imperfections and uncertainty in photon interaction points. A key feature of the pipeline is its ability to to determine the order of the detected photons to construct Compton cones, which intersect with the line of response (LOR) to provide an estimation of the emission point. The pipeline processes 3-{\gamma} PET raw data, reconstructs histo-images by propagating energy and spatial uncertainties along the LOR, and applies a 3-D convolutional neural network (CNN) to refine these intermediate images into high-quality reconstructions. To further enhance image quality, the pipeline leverages both supervised learning and adversarial losses, with the latter preserving fine structural details. Experimental results demonstrate that Direct3{\gamma} consistently outperforms conventional 200-ps time-of-flight (TOF) PET in terms of SSIM and PSNR. This pipeline provides a robust framework for 3{\gamma}-PET imaging, enabling more accurate localization of emission points and setting the stage for improved diagnostic capabilities in clinical and research settings., Comment: 10 pages, 11 figures, 1 table

Published
2024

5. A Hybrid Monte Carlo and Deep Learning Approach for Fast and Generic Photon Beam Dose Calculation

Author

Rousselot, Maxime, Pham, Chi-Hieu, Visvikis, Dimitris, and Bert, Julien

Subjects
Physics - Medical Physics
Abstract

Dosimetry is an essential tool to provide the best and safest radio-therapies to a patient. In this field, Monte-Carlo simulations are considered to be the golden standard for predicting accurately the deposited dose in the body. Such methods are very time-consuming and simpler dose calculation engines, like dose kernel approaches, were created for cases where a fast estimation is necessary. In those approaches, dose distribution maps (or dose kernel) are learned for simple beams geometry, then, a combination of numerous kernels is used to simulate more complex beams. However, those methods often lack personalization and oversimplify the human body, especially for the secondary interactions dose deposition. In this article, we explore the possibility of learning the dose kernel using convolutional neural networks to improve their accuracy towards each different human body. We also highlight the limits of such approaches., Comment: 16 pages, 9 figures

Published
2024

6. Multi-Branch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction

Author

Pinton, Noel Jeffrey, Bousse, Alexandre, Cheze-Le-Rest, Catherine, and Visvikis, Dimitris

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

This paper presents a novel approach for learned synergistic reconstruction of medical images using multi-branch generative models. Leveraging variational autoencoders (VAEs), our model learns from pairs of images simultaneously, enabling effective denoising and reconstruction. Synergistic image reconstruction is achieved by incorporating the trained models in a regularizer that evaluates the distance between the images and the model. We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/computed tomography (CT) datasets, showcasing improved image quality for low-dose imaging. Despite challenges such as patch decomposition and model limitations, our results underscore the potential of generative models for enhancing medical imaging reconstruction., Comment: 12 pages, 16 figures, 2 tables, submitted to IEEE TRPMS

Published
2024

7. CT respiratory motion synthesis using joint supervised and adversarial learning

Author

Cao, Yi-Heng, Bourbonne, Vincent, Lucia, François, Schick, Ulrike, Bert, Julien, Jaouen, Vincent, and Visvikis, Dimitris

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Objective: Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery. Approach: In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude. Main results: Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were $1.97$mm and $0.63$, respectively, for real 4DCT phases and $2.35$mm and $0.71$ for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim)., Comment: to appear in Phys. Med. Biol

Published
2024
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8. Spectral CT Two-step and One-step Material Decomposition using Diffusion Posterior Sampling

Author

Vazia, Corentin, Bousse, Alexandre, Froment, Jacques, Vedel, Béatrice, Vermet, Franck, Wang, Zhihan, Dassow, Thore, Tasu, Jean-Pierre, and Visvikis, Dimitris

Subjects
Physics - Medical Physics
Abstract

This paper proposes a novel approach to spectral computed tomography (CT) material decomposition that uses the recent advances in generative diffusion models (DMs) for inverse problems. Spectral CT and more particularly photon-counting CT (PCCT) can perform transmission measurements at different energy levels which can be used for material decomposition. It is an ill-posed inverse problem and therefore requires regularization. DMs are a class of generative model that can be used to solve inverse problems via diffusion posterior sampling (DPS). In this paper we adapt DPS for material decomposition in a PCCT setting. We propose two approaches, namely Two-step Diffusion Posterior Sampling (TDPS) and One-step Diffusion Posterior Sampling (ODPS). Early results from an experiment with simulated low-dose PCCT suggest that DPSs have the potential to outperform state-of-the-art model-based iterative reconstruction (MBIR). Moreover, our results indicate that TDPS produces material images with better peak signal-to-noise ratio (PSNR) than images produced with ODPS with similar structural similarity (SSIM)., Comment: 5 pages, 3 figures, submitted to EUSIPCO 2024

Published
2024

9. Diffusion Posterior Sampling for Synergistic Reconstruction in Spectral Computed Tomography

Author

Vazia, Corentin, Bousse, Alexandre, Vedel, Béatrice, Vermet, Franck, Wang, Zhihan, Dassow, Thore, Tasu, Jean-Pierre, Visvikis, Dimitris, and Froment, Jacques

Subjects
Physics - Medical Physics
Abstract

Using recent advances in generative artificial intelligence (AI) brought by diffusion models, this paper introduces a new synergistic method for spectral computed tomography (CT) reconstruction. Diffusion models define a neural network to approximate the gradient of the log-density of the training data, which is then used to generate new images similar to the training ones. Following the inverse problem paradigm, we propose to adapt this generative process to synergistically reconstruct multiple images at different energy bins from multiple measurements. The experiments suggest that using multiple energy bins simultaneously improves the reconstruction by inverse diffusion and outperforms state-of-the-art synergistic reconstruction techniques., Comment: 5 pages, 2 figures, IEEE ISBI 2024

Published
2024

10. Fast-Track of F-18 Positron paths simulations using GANs

Author

Mellak, Youness, Chatzipapas, Konstantinos, Bousse, Alexandre, Rest, Catherine Chez-Le, Visvikis, Dimitris, and Bert, Julien

Subjects
Physics - Medical Physics
Abstract

In recent years, the use of Monte Carlo (MC) simulations in the domain of Medical Physics has become a state-of-the-art technology that consumes lots of computational resources for the accurate prediction of particle interactions. The use of generative adversarial network (GAN) has been recently proposed as an alternative to improve the efficiency and extending the applications of computational tools in both medical imaging and therapeutic applications. This study introduces a new approach to simulate positron paths originating from Fluorine 18 (18 F) isotopes through the utilization of GANs. The proposed methodology developed a pure conditional transformer least squares (LS)-GAN model, designed to generate positron paths, and to track their interaction within the surrounding material. Conditioning factors include the pre-determined number of interactions, and the initial momentum of the emitted positrons, as derived from the emission spectrum of 18 F. By leveraging these conditions, the model aims to quickly and accurately simulate electromagnetic interactions of positron paths. Results were compared to the outcome produced with Geant4 Application for Tomography Emission (GATE) MC simulations toolkit. Less than 10 % of difference was observed in the calculation of the mean and maximum length of the path and the 1-D point spread function (PSF) for three different materials (Water, Bone, Lung)., Comment: 5 pages, 3 figures, IEEE ISBI 2024

Published
2024

11. A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches

Author

Bousse, Alexandre, Kandarpa, Venkata Sai Sundar, Shi, Kuangyu, Gong, Kuang, Lee, Jae Sung, Liu, Chi, and Visvikis, Dimitris

Subjects
Physics - Medical Physics
Abstract

Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging., Comment: 16 pages, 6 figures

Published
2023
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12. Uncertainty-Aware Deep Learning Classification for MRI-Based Prostate Cancer Detection

Author

Taguelmimt, Kamilia, Dang, Hong-Phuong, Miranda, Gustavo Andrade, Visvikis, Dimitris, Malavaud, Bernard, Bert, Julien, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ali, Sharib, editor, van der Sommen, Fons, editor, Papież, Bartłomiej Władysław, editor, Ghatwary, Noha, editor, Jin, Yueming, editor, and Kolenbrander, Iris, editor

Published
2025
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13. Optimal Prompting in SAM for Few-Shot and Weakly Supervised Medical Image Segmentation

Author

Siblini, Lara, Andrade-Miranda, Gustavo, Taguelmimt, Kamilia, Visvikis, Dimitris, Bert, Julien, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Deng, Zhongying, editor, Shen, Yiqing, editor, Kim, Hyunwoo J., editor, Jeong, Won-Ki, editor, Aviles-Rivero, Angelica I., editor, He, Junjun, editor, and Zhang, Shaoting, editor

Published
2025
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15. Uconnect: Synergistic Spectral CT Reconstruction with U-Nets Connecting the Energy bins

Author

Wang, Zhihan, Bousse, Alexandre, Vermet, Franck, Froment, Jacques, Vedel, Béatrice, Perelli, Alessandro, Tasu, Jean-Pierre, and Visvikis, Dimitris

Subjects
Physics - Medical Physics
Abstract

Spectral computed tomography (CT) offers the possibility to reconstruct attenuation images at different energy levels, which can be then used for material decomposition. However, traditional methods reconstruct each energy bin individually and are vulnerable to noise. In this paper, we propose a novel synergistic method for spectral CT reconstruction, namely Uconnect. It utilizes trained convolutional neural networks (CNNs) to connect the energy bins to a latent image so that the full binned data is used synergistically. We experiment on two types of low-dose data: simulated and real patient data. Qualitative and quantitative analysis show that our proposed Uconnect outperforms state-of-art model-based iterative reconstruction (MBIR) techniques as well as CNN-based denoising.

Published
2023
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16. Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence

Author

Louis, Thomas, Lucia, François, Cousin, François, Mievis, Carole, Jansen, Nicolas, Duysinx, Bernard, Le Pennec, Romain, Visvikis, Dimitris, Nebbache, Malik, Rehn, Martin, Hamya, Mohamed, Geier, Margaux, Salaun, Pierre-Yves, Schick, Ulrike, Hatt, Mathieu, Coucke, Philippe, Lovinfosse, Pierre, and Hustinx, Roland

Published
2024
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17. Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy

Author

Lucia, François, Louis, Thomas, Cousin, François, Bourbonne, Vincent, Visvikis, Dimitris, Mievis, Carole, Jansen, Nicolas, Duysinx, Bernard, Le Pennec, Romain, Nebbache, Malik, Rehn, Martin, Hamya, Mohamed, Geier, Margaux, Salaun, Pierre-Yves, Schick, Ulrike, Hatt, Mathieu, Coucke, Philippe, Hustinx, Roland, and Lovinfosse, Pierre

Published
2024
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18. On-line Dose Calculation Using Deep Learning for Beams Selection in Non-Coplanar Radiotherapy

Author

Guo, Fang, Okoli, Franklin, Schick, Ulrike, Visvikis, Dimitris, Valeri, Antoine, and Bert, Julien

Subjects
Physics - Medical Physics
Abstract

Non-coplanar Intensity-Modulated Radiation Therapy (IMRT) goes a step further by orienting the gantry carrying the radiation beam and the patient couch in a non-coplanar manner to accurately target the cancer region and better avoid organs-at-risk. The use of a non-coplanar treatment trajectory significantly enhances the degree of freedom and flexibility but increases drastically the complexity of the optimization. In inverse planning optimization the dose contribution for all potential beam directions is usually pre-calculates and pre-loads into the Treatment Planning System (TPS). The size the dose matrix becomes more critical when moving from coplanar IMRT to non-coplanar IMRT since the number of beams increases drastically. A solution would be to calculate "on-the-fly" the dose contribution to each new candidate beam during optimization. This is only possible if a dose calculation engine is fast enough to be used online during optimization iterations, which is not the case in standard method. Therefore, in this work we propose an IMRT optimization scheme using deep learning based dose engine to compute the dose matrix on-line. The proposed deep learning approach will be combined into a simulated-annealing-based optimization method for non-coplanar IMRT. Since the dose engine will compute the dose contribution on-line during the optimization, the final main optimization method requires to keep in memory a very lightweight dose matrix. The proposed method was compared with clinical data showing a good agreement considering dosimetry of the treatment plans. The main advantage of the proposed method was the reduction of the memory storage from 9GB to 10MB during the optimization process., Comment: 12 pages, 6 figures, 2 tables

Published
2023

19. Cross-modal tumor segmentation using generative blending augmentation and self training

Author

Sallé, Guillaume, Conze, Pierre-Henri, Bert, Julien, Boussion, Nicolas, Visvikis, Dimitris, and Jaouen, Vincent

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

\textit{Objectives}: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal image segmentation, where the objective is to segment unlabelled images using previously labelled datasets from other imaging modalities. \textit{Methods}: We propose a cross-modal segmentation method based on conventional image synthesis boosted by a new data augmentation technique called Generative Blending Augmentation (GBA). GBA leverages a SinGAN model to learn representative generative features from a single training image to diversify realistically tumor appearances. This way, we compensate for image synthesis errors, subsequently improving the generalization power of a downstream segmentation model. The proposed augmentation is further combined to an iterative self-training procedure leveraging pseudo labels at each pass. \textit{Results}: The proposed solution ranked first for vestibular schwannoma (VS) segmentation during the validation and test phases of the MICCAI CrossMoDA 2022 challenge, with best mean Dice similarity and average symmetric surface distance measures. \textit{Conclusion and significance}: Local contrast alteration of tumor appearances and iterative self-training with pseudo labels are likely to lead to performance improvements in a variety of segmentation contexts.

Published
2023

20. Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion

Author

Ronrick Da-ano, Gustavo Andrade-Miranda, Olena Tankyevych, Dimitris Visvikis, Pierre-Henri Conze, and Catherine Cheze Le Rest

Subjects
Deep learning, Immunotherapy response, Multi-modal fusion, Medical imaging, Medicine, Science
Abstract

Abstract Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples. Using a single center cohort of 189 patients, our objective was to evaluate various fusion methods that used non-invasive computed tomography (CT) and $$^{18}$$ 18 F-FDG positron emission tomography (PET) images as inputs to various deep learning models to automatically predict PD-L1 in non-small cell lung cancer (NSCLC). We compared three different architectures (ResNet, DenseNet, and EfficientNet) and considered different input data (CT only, PET only, PET/CT early fusion, PET/CT late fusion without as well as with partially and fully shared weights to determine the best model performance. Models were assessed utilizing areas under the receiver operating characteristic curves (AUCs) considering their 95% confidence intervals (CI). The fusion of PET and CT images as input yielded better performance for PD-L1 classification. The different data fusion schemes systematically outperformed their individual counterparts when used as input of the various deep models. Furthermore, early fusion consistently outperformed late fusion, probably as a result of its capacity to capture more complicated patterns by merging PET and CT derived content at a lower level. When we looked more closely at the effects of weight sharing in late fusion architectures, we discovered that while it might boost model stability, it did not always result in better results. This suggests that although weight sharing could be beneficial when modality parameters are similar, the anatomical and metabolic information provided by CT and PET scans are too dissimilar to consistently lead to improved PD-L1 status predictions.

Published
2024
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29. Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence

Author

Thomas Louis, François Lucia, François Cousin, Carole Mievis, Nicolas Jansen, Bernard Duysinx, Romain Le Pennec, Dimitris Visvikis, Malik Nebbache, Martin Rehn, Mohamed Hamya, Margaux Geier, Pierre-Yves Salaun, Ulrike Schick, Mathieu Hatt, Philippe Coucke, Pierre Lovinfosse, and Roland Hustinx

Subjects
Medicine, Science
Abstract

Abstract The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.

Published
2024
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30. Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction

Author

Perelli, Alessandro, Garcia, Suxer Alfonso, Bousse, Alexandre, Tasu, Jean-Pierre, Efthimiadis, Nikolaos, and Visvikis, Dimitris

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Mathematics - Numerical Analysis, Mathematics - Optimization and Control, Physics - Medical Physics
Abstract

Objective. Dual-energy computed tomography (DECT) has the potential to improve contrast, reduce artifacts and the ability to perform material decomposition in advanced imaging applications. The increased number or measurements results with a higher radiation dose and it is therefore essential to reduce either number of projections per energy or the source X-ray intensity, but this makes tomographic reconstruction more ill-posed. Approach. We developed the multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies and we propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features obtained by pre-trained convolutional filters through the convolutional analysis operator learning (CAOL) algorithm. Main results. Extensive experiments with simulated and real computed tomography (CT) data were performed to validate the effectiveness of the proposed methods and we reported increased reconstruction accuracy compared to CAOL and iterative methods with single and joint total-variation (TV) regularization. Significance. Qualitative and quantitative results on sparse-views and low-dose DECT demonstrate that the proposed MCAOL method outperforms both CAOL applied on each energy independently and several existing state-of-the-art model-based iterative reconstruction (MBIR) techniques, thus paving the way for dose reduction., Comment: 23 pages, 11 figures, published in the Physics in Medicine & Biology journal

Published
2022
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32. Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Author

Andrearczyk, Vincent, Oreiller, Valentin, Boughdad, Sarah, Rest, Catherine Chez Le, Elhalawani, Hesham, Jreige, Mario, Prior, John O., Vallières, Martin, Visvikis, Dimitris, Hatt, Mathieu, and Depeursinge, Adrien

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. The challenge is composed of three tasks related to the automatic analysis of PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the automatic segmentation of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the automatic prediction of Progression Free Survival (PFS) from the same FDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVt annotations provided to the participants. The data were collected from six centers for a total of 325 images, split into 224 training and 101 testing cases. The interest in the challenge was highlighted by the important participation with 103 registered teams and 448 result submissions. The best methods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the first task, and a Concordance index (C-index) of 0.7196 and 0.6978 in Tasks 2 and 3, respectively. In all tasks, simplicity of the approach was found to be key to ensure generalization performance. The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used. This potentially obviates the need for GTVt contouring, opening avenues for reproducible and large scale radiomics studies including thousands potential subjects.

Published
2022

33. Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Author

Hering, Alessa, Hansen, Lasse, Mok, Tony C. W., Chung, Albert C. S., Siebert, Hanna, Häger, Stephanie, Lange, Annkristin, Kuckertz, Sven, Heldmann, Stefan, Shao, Wei, Vesal, Sulaiman, Rusu, Mirabela, Sonn, Geoffrey, Estienne, Théo, Vakalopoulou, Maria, Han, Luyi, Huang, Yunzhi, Yap, Pew-Thian, Brudfors, Mikael, Balbastre, Yaël, Joutard, Samuel, Modat, Marc, Lifshitz, Gal, Raviv, Dan, Lv, Jinxin, Li, Qiang, Jaouen, Vincent, Visvikis, Dimitris, Fourcade, Constance, Rubeaux, Mathieu, Pan, Wentao, Xu, Zhe, Jian, Bailiang, De Benetti, Francesca, Wodzinski, Marek, Gunnarsson, Niklas, Sjölund, Jens, Grzech, Daniel, Qiu, Huaqi, Li, Zeju, Thorley, Alexander, Duan, Jinming, Großbröhmer, Christoph, Hoopes, Andrew, Reinertsen, Ingerid, Xiao, Yiming, Landman, Bennett, Huo, Yuankai, Murphy, Keelin, Lessmann, Nikolas, van Ginneken, Bram, Dalca, Adrian V., and Heinrich, Mattias P.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

Published
2021

34. Regularized directional representations for medical image registration

Author

Jaouen, Vincent, Conze, Pierre-Henri, Dardenne, Guillaume, Bert, Julien, and Visvikis, Dimitris

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion. Concurrently to these efforts, an increasing number of works have demonstrated that substantial gains in registration accuracy can also be achieved by aligning structural representations of images rather than images themselves. Following this research path, we propose a new method for mono- and multimodal image registration based on the alignment of regularized vector fields derived from structural information such as gradient vector flow fields, a technique we call \textit{vector field similarity}. Our approach can be combined in a straightforward fashion with any existing registration framework by substituting vector field similarity to intensity-based registration. In our experiments, we show that the proposed approach compares favourably with conventional image alignment on several public image datasets using a diversity of imaging modalities and anatomical locations.

Published
2021

36. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Author

Phelps, Nowell H, Singleton, Rosie K, Zhou, Bin, Heap, Rachel A, Mishra, Anu, Bennett, James E, Paciorek, Christopher J, Lhoste, Victor PF, Carrillo-Larco, Rodrigo M, Stevens, Gretchen A, Rodriguez-Martinez, Andrea, Bixby, Honor, Bentham, James, Di Cesare, Mariachiara, Danaei, Goodarz, Rayner, Archie W, Barradas-Pires, Ana, Cowan, Melanie J, Savin, Stefan, Riley, Leanne M, Aguilar-Salinas, Carlos A, Baker, Jennifer L, Barkat, Amina, Bhutta, Zulfiqar A, Branca, Francesco, Caixeta, Roberta B, Cuschieri, Sarah, Farzadfar, Farshad, Ganapathy, Shubash, Ikeda, Nayu, Iotova, Violeta, Kengne, Andre P, Khang, Young-Ho, Laxmaiah, Avula, Lin, Hsien-Ho, Ma, Jun, Mbanya, Jean Claude N, Miranda, J Jaime, Pradeepa, Rajendra, Rodríguez-Artalejo, Fernando, Sorić, Maroje, Turley, Maria, Wang, Limin, Webster-Kerr, Karen, Aarestrup, Julie, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdeen, Ziad A, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, Abu-Rmeileh, Niveen M, Abubakar Garba, Jamila, Acosta-Cazares, Benjamin, Adam, Ishag, Adamczyk, Marzena, Adams, Robert J, Adu-Afarwuah, Seth, Aekplakorn, Wichai, Afsana, Kaosar, Afzal, Shoaib, Agbor, Valirie N, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Ågren, Åsa, Aguenaou, Hassan, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmad, Noor Ani, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Imran, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Hinai, Halima, Al-Lahou, Badreya, Al-Lawati, Jawad A, Al-Raddadi, Rajaa, Al Asfoor, Deena, Al Hourani, Huda M, Al Qaoud, Nawal M, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Aldwairji, Maryam A, Alexius, Sylvia, Ali, Mohamed M, Alieva, Anna V, Alkandari, Abdullah, Alkerwi, Ala'a, Alkhatib, Buthaina M, Allin, Kristine, Alomary, Shaker A, Alomirah, Husam F, Alshangiti, Arwa M, Alvarez-Pedrerol, Mar, Aly, Eman, Amarapurkar, Deepak N, Amiano Etxezarreta, Pilar, Amoah, John, Amougou, 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Farhad, Pourshams, Akram, Poustchi, Hossein, Price, Alison J, Price, Jacqueline F, Prista, Antonio, Providencia, Rui, Puder, Jardena J, Pudule, Iveta, Puhakka, Solie, Puiu, Maria, Punab, Margus, Qadir, Muhammed S, Qasrawi, Radwan F, Qiao, Qing, Qorbani, Mostafa, Quintana, Hedley K, Quiroga-Padilla, Pedro J, Quoc Bao, Tran, Rach, Stefan, Radic, Ivana, Radisauskas, Ricardas, Rahimikazerooni, Salar, Rahman, Mahfuzar, Rahman, Mahmudur, Raitakari, Olli, Raj, Manu, Rajabov, Tamerlan, Rakhmatulloev, Sherali, Rakovac, Ivo, Ramachandra Rao, Sudha, Ramachandran, Ambady, Ramadan, Otim PC, Ramires, Virgílio V, Ramirez-Zea, Manuel, Ramke, Jacqueline, Ramos, Elisabete, Ramos, Rafel, Rampal, Lekhraj, Rampal, Sanjay, Ramsay, Sheena E, Rangelova, Lalka S, Rarra, Vayia, Rascon-Pacheco, Ramon A, Rashidi, Mohammad-Mahdi, Rech, Cassiano Ricardo, Redon, Josep, Reganit, Paul Ferdinand M, Regecová, Valéria, Renner, Jane DP, Repasy, Judit A, Reuter, Cézane P, Revilla, Luis, Reynolds, Andrew, Rezaei, Negar, 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Alireza, Safarpour, Ali Reza, Safi, Sare, Safiri, Saeid, Saghi, Mohammad Hossien, Saidi, Olfa, Saieva, Calogero, Sakata, Satoko, Saki, Nader, Šalaj, Sanja, Salanave, Benoit, Salazar Martinez, Eduardo, Salhanova, Akkumis, Salmerón, Diego, Salomaa, Veikko, Salonen, Jukka T, Salvetti, Massimo, Samoutian, Margarita, Sánchez-Abanto, Jose, Sánchez Rodríguez, Inés, Sandjaja, Sans, Susana, Santa-Marina, Loreto, Santacruz, Ethel, Santos, Diana A, Santos, Ina S, Santos, Lèlita C, Santos, Maria Paula, Santos, Osvaldo, Santos, Rute, Santos, Tamara R, Saramies, Jouko L, Sardinha, Luis B, Sarrafzadegan, Nizal, Sathish, Thirunavukkarasu, Saum, Kai-Uwe, Savva, Savvas, Savy, Mathilde, Sawada, Norie, Sbaraini, Mariana, Scazufca, Marcia, Schaan, Beatriz D, Schaffrath Rosario, Angelika, Schargrodsky, Herman, Schienkiewitz, Anja, Schindler, Karin, Schipf, Sabine, Schmidt, Börge, Schmidt, Carsten O, Schmidt, Ida Maria, Schneider, Andrea, Schnohr, Peter, Schöttker, Ben, Schramm, Sara, Schramm, Stine, Schröder, Helmut, Schultsz, Constance, Schultz, Gry, Schulze, Matthias B, Schutte, Aletta E, Sebert, Sylvain, Sedaghattalab, Moslem, Selamat, Rusidah, Sember, Vedrana, Sen, Abhijit, Senbanjo, Idowu O, Sepanlou, Sadaf G, Sequera, Guillermo, Serra-Majem, Luis, Servais, Jennifer, Ševčíková, Ľudmila, Sewpaul, Ronel, Shalnova, Svetlana, Shamah-Levy, Teresa, Shamshirgaran, Seyed Morteza, Shanthirani, Coimbatore Subramaniam, Sharafkhah, Maryam, Sharma, Sanjib K, Sharman, Almaz, Shaw, Jonathan E, Shayanrad, Amaneh, Shayesteh, Ali Akbar, Shengelia, Lela, Shi, Zumin, Shibuya, Kenji, Shimizu-Furusawa, Hana, Shimony, Tal, Shiri, Rahman, Shrestha, Namuna, Si-Ramlee, Khairil, Siani, Alfonso, Siantar, Rosalynn, Sibai, Abla M, Sidossis, Labros S, Silitrari, Natalia, Silva, Antonio M, Silva, Caroline Ramos de Moura, Silva, Diego Augusto Santos, Silva, Kelly Samara, Sim, Xueling, Simon, Mary, Simons, Judith, Simons, Leon A, Sjöberg, Agneta, Sjöström, Michael, Skoblina, Elena V, Skoblina, Natalia A, Slazhnyova, Tatyana, Slowikowska-Hilczer, Jolanta, Slusarczyk, Przemysław, Smeeth, Liam, So, Hung-Kwan, Soares, Fernanda Cunha, Sobek, Grzegorz, Sobngwi, Eugène, Sodemann, Morten, Söderberg, Stefan, Soekatri, Moesijanti YE, Soemantri, Agustinus, Sofat, Reecha, Solfrizzi, Vincenzo, Solovieva, Yuliya V, Somi, Mohammad Hossein, Sonestedt, Emily, Song, Yi, Soofi, Sajid, Sørensen, Thorkild IA, Sørgjerd, Elin P, Sossa Jérome, Charles, Soto-Rojas, Victoria E, Soumaré, Aïcha, Sousa-Poza, Alfonso, Sovic, Slavica, Sparboe-Nilsen, Bente, Sparrenberger, Karen, Spencer, Phoebe R, Spinelli, Angela, Spiroski, Igor, Staessen, Jan A, Stamm, Hanspeter, Stang, Andreas, Starc, Gregor, Staub, Kaspar, Stavreski, Bill, Steene-Johannessen, Jostein, Stehle, Peter, Stein, Aryeh D, Steinsbekk, Silje, Stergiou, George S, Stessman, Jochanan, Stevanović, Ranko, Stieber, Jutta, Stöckl, Doris, Stokwiszewski, Jakub, Stoyanova, Ekaterina, Stratton, Gareth, Stronks, Karien, Strufaldi, Maria Wany, Sturua, Lela, Suárez-Medina, Ramón, Suarez-Ortegón, Milton F, Suebsamran, Phalakorn, Sugiyama, Mindy, Suka, Machi, Sulo, Gerhard, Sun, Chien-An, Sun, Liang, Sund, Malin, Sundström, Johan, Sung, Yn-Tz, Sunyer, Jordi, Suriyawongpaisal, Paibul, Sweis, Nabil William G, Swinburn, Boyd A, Sy, Rody G, Sylva, René Charles, Szponar, Lucjan, Tabone, Lorraine, Tai, E Shyong, Takuro, Furusawa, Tambalis, Konstantinos D, Tammesoo, Mari-Liis, Tamosiunas, Abdonas, Tan, Eng Joo, Tang, Xun, Tanrygulyyeva, Maya, Tanser, Frank, Tao, Yong, Tarawneh, Mohammed Rasoul, Tarp, Jakob, Tarqui-Mamani, Carolina B, Taxová Braunerová, Radka, Taylor, Anne, Taylor, Julie, Tchibindat, Félicité, Te Velde, Saskia, Tebar, William R, Tell, Grethe S, Tello, Tania, Tessema, Masresha, Tham, Yih Chung, Thankappan, KR, Theobald, Holger, Theodoridis, Xenophon, Thomas, Nihal, Thorand, Barbara, Thrift, Amanda G, Tichá, Ľubica, Timmermans, Erik J, Tjandrarini, Dwi Hapsari, Tjonneland, Anne, Tolonen, Hanna K, Tolstrup, Janne S, Tomaszewski, Maciej, Topbas, Murat, Topór-Mądry, Roman, Torheim, Liv Elin, Tornaritis, Michael J, Torrent, Maties, Torres-Collado, Laura, Toselli, Stefania, Touloumi, Giota, Traissac, Pierre, Tran, Thi Tuyet-Hanh, Tremblay, Mark S, Triantafyllou, Areti, Trichopoulos, Dimitrios, Trichopoulou, Antonia, Trinh, Oanh TH, Trivedi, Atul, Tshepo, Lechaba, Tsigga, Maria, Tsintavis, Panagiotis, Tsugane, Shoichiro, Tuitele, John, Tuliakova, Azaliia M, Tulloch-Reid, Marshall K, Tullu, Fikru, Tuomainen, Tomi-Pekka, Tuomilehto, Jaakko, Twig, Gilad, Tynelius, Per, Tzala, Evangelia, Tzotzas, Themistoklis, Tzourio, Christophe, Udoji, Nwannedimma, Ueda, Peter, Ugel, Eunice, Ukoli, Flora AM, Ulmer, Hanno, Unal, Belgin, Usupova, Zhamyila, Uusitalo, Hannu MT, Uysal, Nalan, Vaitkeviciute, Justina, Valdivia, Gonzalo, Vale, Susana, Valvi, Damaskini, van Dam, Rob M, van den Born, Bert-Jan, Van der Heyden, Johan, van der Schouw, Yvonne T, Van Herck, Koen, Van Lippevelde, Wendy, Van Minh, Hoang, Van Schoor, Natasja M, van Valkengoed, Irene GM, Vanderschueren, Dirk, Vanuzzo, Diego, Varbo, Anette, Varela-Moreiras, Gregorio, Vargas, Luz Nayibe, Varona-Pérez, Patricia, Vasan, Senthil K, Vasques, Daniel G, Vatasescu, Radu, Vega, Tomas, Veidebaum, Toomas, Velasquez-Melendez, Gustavo, Velika, Biruta, Verloigne, Maïté, Veronesi, Giovanni, Verschuren, WM Monique, Victora, Cesar G, Viegi, Giovanni, Viet, Lucie, Vik, Frøydis N, Vilar, Monica, Villalpando, Salvador, Vioque, Jesus, Viriyautsahakul, Napaphan, Virtanen, Jyrki K, Visser, Marjolein, Visvikis-Siest, Sophie, Viswanathan, Bharathi, Vladulescu, Mihaela, Vlasoff, Tiina, Vocanec, Dorja, Vollenweider, Peter, Völzke, Henry, Vourli, Georgia, Voutilainen, Ari, Vrijheid, Martine, Vrijkotte, Tanja GM, Vuletić, Silvije, Wade, Alisha N, Waldhör, Thomas, Walton, Janette, Wambiya, Elvis OA, Wan Bebakar, Wan Mohamad, Wan Mohamud, Wan Nazaimoon, Wanderley Júnior, Rildo de Souza, Wang, Chongjian, Wang, Huijun, Wang, Ming-Dong, Wang, Ningli, Wang, Qian, Wang, Xiangjun, Wang, Ya Xing, Wang, Ying-Wei, Wannamethee, S Goya, Wareham, Nicholas, Wartha, Olivia, Weber, Adelheid, Wedderkopp, Niels, Weghuber, Daniel, Wei, Wenbin, Weres, Aneta, Werner, Bo, Westbury, Leo D, Whincup, Peter H, Wichstrøm, Lars, Wickramasinghe, Kremlin, Widhalm, Kurt, Widyahening, Indah S, Więcek, Andrzej, Wild, Philipp S, Wilks, Rainford J, Willeit, Johann, Willeit, Peter, Williams, Julianne, Wilsgaard, Tom, Wirth, James P, Wojtyniak, Bogdan, Woldeyohannes, Meseret, Wolf, Kathrin, Wong-McClure, Roy A, Wong, Andrew, Wong, Emily B, Wong, Jyh Eiin, Wong, Tien Yin, Woo, Jean, Woodward, Mark, Wu, Frederick C, Wu, Hon-Yen, Wu, Jianfeng, Wu, Li Juan, Wu, Shouling, Wyszyńska, Justyna, Xu, Haiquan, Xu, Liang, Yaacob, Nor Azwany, Yamborisut, Uruwan, Yan, Li, Yan, Weili, Yang, Ling, Yang, Xiaoguang, Yang, Yang, Yardim, Nazan, Yasuharu, Tabara, Yépez García, Martha, Yiallouros, Panayiotis K, Yngve, Agneta, Yoosefi, Moein, Yoshihara, Akihiro, Yotov, Yoto, You, Qi Sheng, You, San-Lin, Younger-Coleman, Novie O, Yu, Yu-Ling, Yu, Yunjiang, Yusof, Safiah Md, Yusoff, Ahmad Faudzi, Zaccagni, Luciana, Zafiropulos, Vassilis, Zainuddin, Ahmad A, Zakavi, Seyed Rasoul, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Zamrazilová, Hana, Zapata, Maria Elisa, Zargar, Abdul Hamid, Zaw, Ko Ko, Zayed, Ayman A, Zdrojewski, Tomasz, Żegleń, Magdalena, Zejglicova, Kristyna, Zeljkovic Vrkic, Tajana, Zeng, Yi, Zentai, Andrea, Zhang, Bing, Zhang, Luxia, Zhang, Zhen-Yu, Zhao, Dong, Zhao, Ming-Hui, Zhao, Wenhua, Zhecheva, Yanitsa V, Zhen, Shiqi, Zheng, Wei, Zheng, Yingfeng, Zholdin, Bekbolat, Zhou, Maigeng, Zhu, Dan, Zimmet, Paul, Zins, Marie, Zitt, Emanuel, Zocalo, Yanina, Zoghlami, Nada, Zuñiga Cisneros, Julio, Zuziak, Monika, and Ezzati, Majid

Published
2024
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37. Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images

Author

Iantsen, Andrei, Visvikis, Dimitris, and Hatt, Mathieu

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies. In this work, we contributed an automated approach for Head and Neck (H&N) primary tumor segmentation in combined positron emission tomography / computed tomography (PET/CT) images in the context of the MICCAI 2020 Head and Neck Tumor segmentation challenge (HECKTOR). Our model was designed on the U-Net architecture with residual layers and supplemented with Squeeze-and-Excitation Normalization. The described method achieved competitive results in cross-validation (DSC 0.745, precision 0.760, recall 0.789) performed on different centers, as well as on the test set (DSC 0.759, precision 0.833, recall 0.740) that allowed us to win first prize in the HECKTOR challenge among 21 participating teams. The full implementation based on PyTorch and the trained models are available at https://github.com/iantsen/hecktor, Comment: 7 pages, 2 figures, 2 tables

Published
2021
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38. DUG-RECON: A Framework for Direct Image Reconstruction using Convolutional Generative Networks

Author

Kandarpa, V. S. S., Bousse, Alexandre, Benoit, Didier, and Visvikis, Dimitris

Subjects
Physics - Medical Physics
Abstract

This paper explores convolutional generative networks as an alternative to iterative reconstruction algorithms in medical image reconstruction. The task of medical image reconstruction involves mapping of projection main data collected from the detector to the image domain. This mapping is done typically through iterative reconstruction algorithms which are time consuming and computationally expensive. Trained deep learning networks provide faster outputs as proven in various tasks across computer vision. In this work we propose a direct reconstruction framework exclusively with deep learning architectures. The proposed framework consists of three segments, namely denoising, reconstruction and super resolution. The denoising and the super resolution segments act as processing steps. The reconstruction segment consists of a novel double U-Net generator (DUG) which learns the sinogram-to-image transformation. This entire network was trained on positron emission tomography (PET) and computed tomography (CT) images. The reconstruction framework approximates two-dimensional (2-D) mapping from projection domain to image domain. The architecture proposed in this proof-of-concept work is a novel approach to direct image reconstruction; further improvement is required to implement it in a clinical setting., Comment: The code associated with the paper can be found at https://github.com/sai-sundar/DUG-RECON.git

Published
2020
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40. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT

Author

Andrearczyk, Vincent, Oreiller, Valentin, Abobakr, Moamen, Akhavanallaf, Azadeh, Balermpas, Panagiotis, Boughdad, Sarah, Capriotti, Leo, Castelli, Joel, Cheze Le Rest, Catherine, Decazes, Pierre, Correia, Ricardo, El-Habashy, Dina, Elhalawani, Hesham, Fuller, Clifton D., Jreige, Mario, Khamis, Yomna, La Greca, Agustina, Mohamed, Abdallah, Naser, Mohamed, Prior, John O., Ruan, Su, Tanadini-Lang, Stephanie, Tankyevych, Olena, Salimi, Yazdan, Vallières, Martin, Vera, Pierre, Visvikis, Dimitris, Wahid, Kareem, Zaidi, Habib, Hatt, Mathieu, Depeursinge, Adrien, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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42. Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer

Author

Lucia, François, Bourbonne, Vincent, Pleyers, Clémence, Dupré, Pierre-François, Miranda, Omar, Visvikis, Dimitris, Pradier, Olivier, Abgral, Ronan, Mervoyer, Augustin, Classe, Jean-Marc, Rousseau, Caroline, Vos, Wim, Hermesse, Johanne, Gennigens, Christine, De Cuypere, Marjolein, Kridelka, Frédéric, Schick, Ulrike, Hatt, Mathieu, Hustinx, Roland, and Lovinfosse, Pierre

Published
2023
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46. Advanced Monte Carlo simulations of emission tomography imaging systems with GATE

Author

Sarrut, David, Bała, Mateusz, Bardiès, Manuel, Bert, Julien, Chauvin, Maxime, Chatzipapas, Konstantinos, Dupont, Mathieu, Etxebeste, Ane, Fanchon, Louise M, Jan, Sébastien, Kayal, Gunjan, Kirov, Assen S, Kowalski, Paweł, Krzemien, Wojciech, Labour, Joey, Lenz, Mirjam, Loudos, George, Mehadji, Brahim, Ménard, Laurent, Morel, Christian, Papadimitroulas, Panagiotis, Rafecas, Magdalena, Salvadori, Julien, Seiter, Daniel, Stockhoff, Mariele, Testa, Etienne, Trigila, Carlotta, Pietrzyk, Uwe, Vandenberghe, Stefaan, Verdier, Marc-Antoine, Visvikis, Dimitris, Ziemons, Karl, Zvolský, Milan, and Roncali, Emilie

Subjects
Synchrotrons and Accelerators, Physical Sciences, Biomedical Imaging, Artificial Intelligence, Computer Simulation, Monte Carlo Method, Software, Tomography, X-Ray Computed, Monte Carlo simulation, Positron emission tomography, Single-photon emission computed tomography, Compton Camera, Other Physical Sciences, Biomedical Engineering, Clinical Sciences, Nuclear Medicine & Medical Imaging, Medical and biological physics
Abstract

Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE. Additionally, some methodological developments that are not specific for imaging but that can improve detector modeling and provide computation time gains, such as Variance Reduction Techniques and Artificial Intelligence integration, are described and discussed.

Published
2021

50. Incidence of Disease Recurrence in Patients With Colon and Upper Rectum Adenocarcinoma Stage II and III Receiving Adjuvant Capecitabine Monotherapy: Do Number of Chemotherapy Cycles and Relative Dose Intensity of the Drug Play a Role?

Author

Sgouros, Joseph, Gkoura, Stefania, Spathas, Nikolaos, Tzoudas, Fotios, Karampinos, Konstantinos, Miaris, Nikolaos, Visvikis, Anastasios, Dessypris, Nick, Mauri, Davide, Aravantinos, Gerasimos, Theodoropoulos, Ilias, Stamoulis, George, and Samantas, Epaminondas

Published
2023
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