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Over the last decade, convolutional neural networks have emerged and advanced the state-of-the-art in various image analysis and computer vision applications. The performance of 2D image classification networks is constantly improving and being trained on databases made of millions of natural images. Conversely, in the field of medical image analysis, the progress is also remarkable but has mainly slowed down due to the relative lack of annotated data and besides, the inherent constraints related to the acquisition process. These limitations are even more pronounced given the volumetry of medical imaging data. In this paper, we introduce an efficient way to transfer the efficiency of a 2D classification network trained on natural images to 2D, 3D uni- and multi-modal medical image segmentation applications. In this direction, we designed novel architectures based on two key principles: weight transfer by embedding a 2D pre-trained encoder into a higher dimensional U-Net, and dimensional transfer by expanding a 2D segmentation network into a higher dimension one. The proposed networks were tested on benchmarks comprising different modalities: MR, CT, and ultrasound images. Our 2D network ranked first on the CAMUS challenge dedicated to echo-cardiographic data segmentation and surpassed the state-of-the-art. Regarding 2D/3D MR and CT abdominal images from the CHAOS challenge, our approach largely outperformed the other 2D-based methods described in the challenge paper on Dice, RAVD, ASSD, and MSSD scores and ranked third on the online evaluation platform. Our 3D network applied to the BraTS 2022 competition also achieved promising results, reaching an average Dice score of 91.69% (91.22%) for the whole tumor, 83.23% (84.77%) for the tumor core and 81.75% (83.88%) for enhanced tumor using the approach based on weight (dimensional) transfer. Experimental and qualitative results illustrate the effectiveness of our methods for multi-dimensional medical image segmentation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Recent clinical research studies in forensic identification have highlighted the interest in sphenoid sinus anatomical characterization. Their pneumatization, well known as extremely variable in degrees and directions, could contribute to the radiologic identification, especially if dental records, fingerPrints, or DNA samples are not available. In this paper, we present a new approach for automatic person identification based on sphenoid sinus features extracted from computed tomography (CT) images of the skull. First, we present a new approach for fully automatic 3D reconstruction of the sphenoid hemisinuses which combines the fuzzy c-means method and mathematical morphology operations to detect and segment the object of interest. Second, deep shape features are extracted from both hemisinuses using a dilated residual version of a stacked convolutional auto-encoder. The obtained binary segmentation masks are thus hierarchically mapped into a compact and low-dimensional space preserving their semantic similarity. We finally employ the ℓ 2 distance to recognize the sphenoid sinus and therefore identify the person. This novel sphenoid sinus recognition method obtained 100% of identification accuracy when applied on a dataset composed of 85 CT scans stemming from 72 individuals. Automatic Forensic Identification using 3D Sphenoid Sinus Segmentation and Deep Characterization from Dilated Residual Auto-Encoders.

In this paper, we propose a method for non-invasively measuring three-dimensional in vivo kinematics of the ankle joint from a dynamic MRI acquisition of a single range-of-motion cycle. The proposed approach relies on an intensity-based registration method to estimate motion from multi-plane dynamic MRI data. Our approach recovers not only the movement of the skeleton, but also the possibly non-rigid temporal deformation of the joint. First, the rigid motion of each ankle bone is estimated. Second, a four-dimensional (3D+time) high-resolution dynamic MRI sequence is estimated through the use of the log-euclidean framework for the computation of temporal dense deformation fields. This approach has been then applied and evaluated on in vivo dynamic MRI data acquired for a pilot study on six healthy pediatric cohort in order to establish in vivo normative joint biomechanics. Results demonstrate the robustness of the proposed pipeline and very promising high resolution visualization of the ankle joint., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Purpose: To evaluate the agreement and diagnostic accuracy of Contrast enhanced magnetic resonance angiography (CE-MRA), Doppler ultrasound (DUS) and Digital subtraction angiography (DSA) in the assessment of carotid stenosis., Methods: DUS, CE-MRA and DSA were performed in 56 patients included in the Carotide-angiographie par résonance magnétique-échographie-doppler-angioscanner (CARMEDAS) multicenter study with a carotid stenosis ≥ 50%. Three readers evaluated stenoses on CE-MRA and DSA (NASCET criteria). Velocities criteria were used for stenosis estimation on DUS., Results: CE-MRA had a sensitivity and specificity of 96-98% and 66-83% respectively for carotid stenoses ≥ 50% and a sensitivity and specificity of 94% and 76-84% respectively for carotid stenoses ≥ 70%. The interobserver agreement of CE-MRA was excellent, except for moderate stenoses (50-69%). DUS had a sensitivity and specificity of 88 and 75% respectively for carotid stenoses ≥ 50% and a sensitivity and specificity of 83 and 86% respectively for carotid stenoses ≥ 70%. Combined concordant CE-MRA and DUS had a sensitivity and specificity of 100 and 85-90% respectively for carotid stenoses ≥ 50% and a sensitivity and specificity of 96-100% and 80-87% respectively for carotid stenoses ≥ 70%. The positive predictive value of the association CE-MRA and DUS for carotid stenoses ≥ 70% is calculated between 77 and 82% while the negative predictive value is calculated between 97 and 100%. CE-MRA and DUS have concordant findings in 63-72%, and the overestimations cases were recorded only for carotid stenosis ≤ 69%., Conclusion: Combined DUS-CE-MRA is excellent for evaluation of severe stenosis but remains debatable in moderate stenosis (50-69%) due to the risk of overestimations., (Copyright © 2010 Elsevier Masson SAS. All rights reserved.)