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2,217 results on '"Bi, Lei"'

353. AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images

Author

Meng, Mingyuan, Gu, Bingxin, Fulham, Michael, Song, Shaoli, Feng, Dagan, Bi, Lei, and Kim, Jinman

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)
Abstract

Survival prediction is a major concern for cancer management. Deep survival models based on deep learning have been widely adopted to perform end-to-end survival prediction from medical images. Recent deep survival models achieved promising performance by jointly performing tumor segmentation with survival prediction, where the models were guided to extract tumor-related information through Multi-Task Learning (MTL). However, these deep survival models have difficulties in exploring out-of-tumor prognostic information. In addition, existing deep survival models are unable to effectively leverage multi-modality images. Empirically-designed fusion strategies were commonly adopted to fuse multi-modality information via task-specific manually-designed networks, thus limiting the adaptability to different scenarios. In this study, we propose an Adaptive Multi-modality Segmentation-to-Survival model (AdaMSS) for survival prediction from PET/CT images. Instead of adopting MTL, we propose a novel Segmentation-to-Survival Learning (SSL) strategy, where our AdaMSS is trained for tumor segmentation and survival prediction sequentially in two stages. This strategy enables the AdaMSS to focus on tumor regions in the first stage and gradually expand its focus to include other prognosis-related regions in the second stage. We also propose a data-driven strategy to fuse multi-modality information, which realizes adaptive optimization of fusion strategies based on training data during training. With the SSL and data-driven fusion strategies, our AdaMSS is designed as an adaptive model that can self-adapt its focus regions and fusion strategy for different training stages. Extensive experiments with two large clinical datasets show that our AdaMSS outperforms state-of-the-art survival prediction methods., Under Review

Published
2023

354. CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images

Author

Xue, Yuxin, Peng, Yige, Bi, Lei, Feng, Dagan, and Kim, Jinman

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Positron emission tomography (PET) is the most sensitive molecular imaging modality routinely applied in our modern healthcare. High radioactivity caused by the injected tracer dose is a major concern in PET imaging and limits its clinical applications. However, reducing the dose leads to inadequate image quality for diagnostic practice. Motivated by the need to produce high quality images with minimum low-dose, Convolutional Neural Networks (CNNs) based methods have been developed for high quality PET synthesis from its low-dose counterparts. Previous CNNs-based studies usually directly map low-dose PET into features space without consideration of different dose reduction level. In this study, a novel approach named CG-3DSRGAN (Classification-Guided Generative Adversarial Network with Super Resolution Refinement) is presented. Specifically, a multi-tasking coarse generator, guided by a classification head, allows for a more comprehensive understanding of the noise-level features present in the low-dose data, resulting in improved image synthesis. Moreover, to recover spatial details of standard PET, an auxiliary super resolution network - Contextual-Net - is proposed as a second-stage training to narrow the gap between coarse prediction and standard PET. We compared our method to the state-of-the-art methods on whole-body PET with different dose reduction factors (DRFs). Experiments demonstrate our method can outperform others on all DRF.

Published
2023

366. The Liver Tumor Segmentation Benchmark (LiTS)

Author

Bilic, Patrick, primary, Christ, Patrick, additional, Li, Hongwei Bran, additional, Vorontsov, Eugene, additional, Ben-Cohen, Avi, additional, Kaissis, Georgios, additional, Szeskin, Adi, additional, Jacobs, Colin, additional, Mamani, Gabriel Efrain Humpire, additional, Chartrand, Gabriel, additional, Lohöfer, Fabian, additional, Holch, Julian Walter, additional, Sommer, Wieland, additional, Hofmann, Felix, additional, Hostettler, Alexandre, additional, Lev-Cohain, Naama, additional, Drozdzal, Michal, additional, Amitai, Michal Marianne, additional, Vivanti, Refael, additional, Sosna, Jacob, additional, Ezhov, Ivan, additional, Sekuboyina, Anjany, additional, Navarro, Fernando, additional, Kofler, Florian, additional, Paetzold, Johannes C., additional, Shit, Suprosanna, additional, Hu, Xiaobin, additional, Lipková, Jana, additional, Rempfler, Markus, additional, Piraud, Marie, additional, Kirschke, Jan, additional, Wiestler, Benedikt, additional, Zhang, Zhiheng, additional, Hülsemeyer, Christian, additional, Beetz, Marcel, additional, Ettlinger, Florian, additional, Antonelli, Michela, additional, Bae, Woong, additional, Bellver, Míriam, additional, Bi, Lei, additional, Chen, Hao, additional, Chlebus, Grzegorz, additional, Dam, Erik B., additional, Dou, Qi, additional, Fu, Chi-Wing, additional, Georgescu, Bogdan, additional, Giró-i-Nieto, Xavier, additional, Gruen, Felix, additional, Han, Xu, additional, Heng, Pheng-Ann, additional, Hesser, Jürgen, additional, Moltz, Jan Hendrik, additional, Igel, Christian, additional, Isensee, Fabian, additional, Jäger, Paul, additional, Jia, Fucang, additional, Kaluva, Krishna Chaitanya, additional, Khened, Mahendra, additional, Kim, Ildoo, additional, Kim, Jae-Hun, additional, Kim, Sungwoong, additional, Kohl, Simon, additional, Konopczynski, Tomasz, additional, Kori, Avinash, additional, Krishnamurthi, Ganapathy, additional, Li, Fan, additional, Li, Hongchao, additional, Li, Junbo, additional, Li, Xiaomeng, additional, Lowengrub, John, additional, Ma, Jun, additional, Maier-Hein, Klaus, additional, Maninis, Kevis-Kokitsi, additional, Meine, Hans, additional, Merhof, Dorit, additional, Pai, Akshay, additional, Perslev, Mathias, additional, Petersen, Jens, additional, Pont-Tuset, Jordi, additional, Qi, Jin, additional, Qi, Xiaojuan, additional, Rippel, Oliver, additional, Roth, Karsten, additional, Sarasua, Ignacio, additional, Schenk, Andrea, additional, Shen, Zengming, additional, Torres, Jordi, additional, Wachinger, Christian, additional, Wang, Chunliang, additional, Weninger, Leon, additional, Wu, Jianrong, additional, Xu, Daguang, additional, Yang, Xiaoping, additional, Yu, Simon Chun-Ho, additional, Yuan, Yading, additional, Yue, Miao, additional, Zhang, Liping, additional, Cardoso, Jorge, additional, Bakas, Spyridon, additional, Braren, Rickmer, additional, Heinemann, Volker, additional, Pal, Christopher, additional, Tang, An, additional, Kadoury, Samuel, additional, Soler, Luc, additional, van Ginneken, Bram, additional, Greenspan, Hayit, additional, Joskowicz, Leo, additional, and Menze, Bjoern, additional

Published
2023
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396. Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer

Author

Meng, Mingyuan, Bi, Lei, Fulham, Michael, Feng, Dagan, and Kim, Jinman

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)
Abstract

Survival prediction is crucial for cancer patients as it provides early prognostic information for treatment planning. Recently, deep survival models based on deep learning and medical images have shown promising performance for survival prediction. However, existing deep survival models are not well developed in utilizing multi-modality images (e.g., PET-CT) and in extracting region-specific information (e.g., the prognostic information in Primary Tumor (PT) and Metastatic Lymph Node (MLN) regions). In view of this, we propose a merging-diverging learning framework for survival prediction from multi-modality images. This framework has a merging encoder to fuse multi-modality information and a diverging decoder to extract region-specific information. In the merging encoder, we propose a Hybrid Parallel Cross-Attention (HPCA) block to effectively fuse multi-modality features via parallel convolutional layers and cross-attention transformers. In the diverging decoder, we propose a Region-specific Attention Gate (RAG) block to screen out the features related to lesion regions. Our framework is demonstrated on survival prediction from PET-CT images in Head and Neck (H&N) cancer, by designing an X-shape merging-diverging hybrid transformer network (named XSurv). Our XSurv combines the complementary information in PET and CT images and extracts the region-specific prognostic information in PT and MLN regions. Extensive experiments on the public dataset of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR 2022) demonstrate that our XSurv outperforms state-of-the-art survival prediction methods., Early Accepted at International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023)

Published
2023
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400. Multi-stage Thresholded Region Classification for Whole-Body PET-CT Lymphoma Studies

Author

Bi, Lei, Kim, Jinman, Feng, Dagan, Fulham, Michael, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Golland, Polina, editor, Hata, Nobuhiko, editor, Barillot, Christian, editor, Hornegger, Joachim, editor, and Howe, Robert, editor

Published
2014
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