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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Lee, Jieun Oh, Kwanseok Shen, Dinggang Suk, Heung-Il Korea Univ Dept Artificial Intelligence Seoul South Korea Korea Univ Dept Brain & Cognit Engn Seoul South Korea ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai Peoples R China

ISBN: (纸本)9783031164439;9783031164422

An increase in signal-to-noise ratio (SNR) and susceptibility-induced contrast at higher field strengths, e.g., 7T, is crucial for medical image analysis by providing better insights for the pathophysiology, diagnosis, and treatment of several disease entities. However, it is difficult to obtain 7T images in real clinical practices due to the high cost and low accessibility. In this paper, we propose a novel knowledge keeper network (KKN) to guide brain tissue segmentation by taking advantage of 7T representations without explicitly using 7T images. By extracting features of a 3T input image substantially and then transforming them to 7T features via knowledge distillation (KD), our method achieves deriving 7T-like representations from a given 3T image and exploits them for tissue segmentation. On two independent datasets, we evaluated our method's validity in qualitative and quantitative manners on 7T-like image synthesis and 7T-guided tissue segmentation by comparing with the comparative methods in the literature.

关键词： Brain tissue segmentation Knowledge distillation medical image synthesis 7T MRI

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Non-iterative Coarse-to-Fine Registration Based on Single-Pass Deep Cumulative Learning 25th

Non-iterative Coarse-to-Fine Registration Based on Single-Pa...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Univ Sydney Sch Comp Sci Sydney Australia Shanghai Jiao Tong Univ Med X Res Inst Shanghai Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Deformable image registration is a crucial step in medical image analysis for finding a non-linear spatial transformation between a pair of fixed and moving images. Deep registration methods based on Convolutional Neural Networks (CNNs) have been widely used as they can perform image registration in a fast and end-to-end manner. However, these methods usually have limited performance for image pairs with large deformations. Recently, iterative deep registration methods have been used to alleviate this limitation, where the transformations are iteratively learned in a coarse-to-fine manner. However, iterative methods inevitably prolong the registration runtime, and tend to learn separate image features for each iteration, which hinders the features from being leveraged to facilitate the registration at later iterations. In this study, we propose a Non-Iterative Coarse-to-finE registration Network (NICE-Net) for deformable image registration. In the NICE-Net, we propose: (i) a Single-pass Deep Cumulative Learning (SDCL) decoder that can cumulatively learn coarse-to-fine transformations within a single pass (iteration) of the network, and (ii) a Selectively-propagated Feature Learning (SFL) encoder that can learn common image features for the whole coarse-to-fine registration process and selectively propagate the features as needed. Extensive experiments on six public datasets of 3D brain Magnetic Resonance Imaging (MRI) show that our proposed NICE-Net can outperform state-of-the-art iterative deep registration methods while only requiring similar runtime to non-iterative methods.

关键词： image registration Cumulative learning Large deformations

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Self-supervised Depth Estimation in Laparoscopic image Using 3D Geometric Consistency 25th

Self-supervised Depth Estimation in Laparoscopic Image Using...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Huang, Baoru Zheng, Jian-Qing Nguyen, Anh Xu, Chi Gkouzionis, Ioannis Vyas, Kunal Tuch, David Giannarou, Stamatia Elson, Daniel S. Imperial Coll London Hamlyn Ctr Robot Surg London England Imperial Coll London Dept Surg & Canc London England Univ Oxford Kennedy Inst Rheumatol Oxford England Univ Oxford Big Data Inst Oxford England Univ Liverpool Dept Comp Sci Liverpool Merseyside England Lightpoint Med Ltd Chesham England

ISBN: (纸本)9783031164491;9783031164484

Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system. Since per-pixel depth ground truth is difficult to acquire for laparoscopic image data, it is rarely possible to apply supervised depth estimation to surgical applications. As an alternative, self-supervised methods have been introduced to train depth estimators using only synchronized stereo image pairs. However, most recent work focused on the left-right consistency in 2D and ignored valuable inherent 3D information on the object in real world coordinates, meaning that the left-right 3D geometric structural consistency is not fully utilized. To overcome this limitation, we present M3Depth, a self-supervised depth estimator to leverage 3D geometric structural information hidden in stereo pairs while keeping monocular inference. the method also removes the influence of border regions unseen in at least one of the stereo images via masking, to enhance the correspondences between left and right images in overlapping areas. Extensive experiments show that our method outperforms previous self-supervised approaches on both a public dataset and a newly acquired dataset by a large margin, indicating a good generalization across different samples and laparoscopes.

关键词： Self-supervised monocular depth estimation Laparoscopic images 3D geometric consistency

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PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images

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medical image ANALYSIS 2025年 99卷 103353页

作者： Bai, Jieyun Zhou, Zihao Ou, Zhanhong Koehler, Gregor Stock, Raphael Maier-Hein, Klaus Elbatel, Marawan Marti, Robert Li, Xiaomeng Qiu, Yaoyang Gou, Panjie Chen, Gongping Zhao, Lei Zhang, Jianxun Dai, Yu Wang, Fangyijie Silvestre, Guenole Curran, Kathleen Sun, Hongkun Xu, Jing Cai, Pengzhou Jiang, Lu Lan, Libin Ni, Dong Zhong, Mei Chen, Gaowen Campello, Victor M. Lu, Yaosheng Lekadir, Karim Jinan Univ Guangdong Prov Key Lab Tradit Chinese Med Informat Guangzhou Peoples R China Univ Auckland Auckland Bioengn Inst Private Bag 92019 Auckland 1142 New Zealand German Canc Res Ctr Div Med Image Comp Heidelberg Germany Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Univ Girona Comp Vis & Robot Grp Girona Spain Canon Med Syst China Co Ltd Beijing Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Univ Coll Dublin Sch Med Dublin Ireland Univ Coll Dublin Sch Comp Sci Dublin Ireland Zhejiang Gongshang Univ Sch Stat & Math Hangzhou Peoples R China Chongqing Univ Technol Sch Comp Sci & Engn Chongqing Peoples R China Shenzhen Univ Hlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Hlth Sci Ctr Guangdong Prov Key Lab Biomed Measurements & Ultra Shenzhen Peoples R China Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Shenzhen Peoples R China Southern Med Univ NanFang Hosp Guangzhou Peoples R China Southern Med Univ Zhujiang Hosp Guangzhou Peoples R China Univ Barcelona Dept Matemat & Informat Barcelona Spain INst Catalana Recerca & Estudis Avancats ICREA Barcelona Spain

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the international Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. this requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. the utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th international conference on medical image computing and computer assisted intervention (MICCAI 2023). this challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. the scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. these algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. the top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging.

关键词： Intrapartum ultrasound Fetal biometry Deep learning Challenge Angle of progress image segmentation

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Test-Time Adaptation with Shape Moments for image Segmentation 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Bateson, Mathilde Lombaert, Herve Ben Ayed, Ismail ETS Montreal Montreal PQ Canada

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Supervised learning is well-known to fail at generalization under distribution shifts. In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few (or even a single) subject(s). We investigate test-time single-subject adaptation for segmentation, and propose a shape-guided entropy minimization objective for tackling this task. During inference for a single testing subject, our loss is minimized with respect to the batch normalization's scale and bias parameters. We show the potential of integrating various shape priors to guide adaptation to plausible solutions, and validate our method in two challenging scenarios: MRI-to-CT adaptation of cardiac segmentation and cross-site adaptation of prostate segmentation. Our approach exhibits substantially better performances than the existing test-time adaptation methods. Even more surprisingly, it fares better than state-of-the-art domain adaptation methods, although it forgoes training on additional target data during adaptation. Our results question the usefulness of training on target data in segmentation adaptation, and points to the substantial effect of shape priors on test-time inference. Our framework can be readily used for integrating various priors and for adapting any segmentation network. the code is publicly available (https://*** mathilde-b/TTA).

关键词： Test-time adaptation Segmentation Shape moments Deep networks Entropy minimization

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Vulnerability Analysis of Deep Learning Model for OCTA image Classification 5th

Vulnerability Analysis of Deep Learning Model for OCTA Ima...

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5th international conference on Advances in Information Communication Technology and computing, AICTC 2024

作者： Trivedi, Munesh Chandra Bharadwaj, Siddhant Department of Computer Science and Engineering Pandit Sunderlal Sharma Central Institute of Vocational Education Bhopal India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal India

ISBN: (纸本)9789819761029

the prevalence of Deep Learning in healthcare has revolutionized medical diagnostics, yet the vulnerability of these models to adversarial attacks threatens their security. this study investigates the susceptibility of a sophisticated deep learning model, trained to classify OCTA images as healthy or diabetic, to adversarial perturbations. Despite achieving high accuracy on unperturbed data, the model remains vulnerable to adversarial noise. the study employs techniques like Project Gradient Descent (PGD) and Fast Gradient Sign Method (FGSM) to generate adversarial examples and tests their efficacy against the model. Results show that even minor perturbations can lead to misclassification, emphasizing the need for adversarial robustness in healthcare models. As healthcare decisions are critical, incorporating adversarial training is must to mitigate the impact of adversarial vulnerabilities in deep learning-based medical diagnoses. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Diabetic Foot Ulcer Grand Challenge 2022 Summary 3rd

Diabetic Foot Ulcer Grand Challenge 2022 Summary

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3rd Diabetic Foot Ulcers Grand Challenge, DFUC 2022, held in Conjunction with the 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

作者： Kendrick, Connah Cassidy, Bill Reeves, Neil D. Pappachan, Joseph M. O’Shea, Claire Chandrabalan, Vishnu Yap, Moi Hoon Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Musculoskeletal Science and Sports Medicine Manchester Metropolitan University ManchesterM1 5GD United Kingdom Lancashire Teaching Hospitals NHS Foundation Trust PrestonPR2 9HT United Kingdom Waikato District Health Board Hamilton3240 New Zealand

ISBN: (纸本)9783031263538

the Diabetic Foot Ulcer Challenge 2022 focused on the task of diabetic foot ulcer segmentation, based on the work completed in previous DFU challenges. the challenge provided 4000 images of full-view foot ulcer images together with corresponding delineation of ulcer regions. this paper provides an overview of the challenge, a summary of the methods proposed by the challenge participants, the results obtained from each technique, and a comparison of the challenge results. the best-performing network was a modified HarDNet-MSEG, with a Dice score of 0.7287. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

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Online Easy Example Mining for Weakly-Supervised Gland Segmentation from Histology images 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Li, Yi Yu, Yiduo Zou, Yiwen Xiang, Tianqi Li, Xiaomeng Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China HKUST Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Developing an AI-assisted gland segmentation method from histology images is critical for automatic cancer diagnosis and prognosis;however, the high cost of pixel-level annotations hinders its applications to broader diseases. Existing weakly-supervised semantic segmentation methods in computer vision achieve degenerative results for gland segmentation, since the characteristics and problems of glandular datasets are different from general object datasets. We observe that, unlike natural images, the key problem with histology images is the confusion of classes owning to morphological homogeneity and low color contrast among different tissues. To this end, we propose a novel method Online Easy Example Mining (OEEM) that encourages the network to focus on credible supervision signals rather than noisy signals, therefore mitigating the influence of inevitable false predictions in pseudo-masks. According to the characteristics of glandular datasets, we design a strong framework for gland segmentation. Our results exceed many fully-supervised methods and weakly-supervised methods for gland segmentation over 4.6% and 6.04% at mIoU, respectively. Code is available at https://github. com/xmed-lab/OEEM.

关键词： Online Easy Example Mining Histology image Gland segmentation Wealy-supervised semantic segmentation

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CACTUSS: Common Anatomical CT-US Space for US Examinations 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Velikova, Yordanka Simson, Walter Salehi, Mehrdad Azampour, Mohammad Farid Paprottka, Philipp Navab, Nassir Tech Univ Munich Comp Aided Med Procedures Munich Germany Klinikum Rechts Der Isar Intervent Radiol Munich Germany Sharif Univ Technol Dept Elect Engn Tehran Iran Johns Hopkins Univ Comp Aided Med Procedures Baltimore MD USA

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel. Abdominal ultrasound has been utilized for diagnostics, but due to its limited image quality and operator dependency, CT scans are usually required for monitoring and treatment planning. Recently, abdominal CT datasets have been successfully utilized to train deep neural networks for automatic aorta segmentation. Knowledge gathered from this solved task could therefore be leveraged to improve US segmentation for AAA diagnosis and monitoring. To this end, we propose CACTUSS: a common anatomical CT-US space, which acts as a virtual bridge between CT and US modalities to enable automatic AAA screening sonography. CACTUSS makes use of publicly available labelled data to learn to segment based on an intermediary representation that inherits properties from both US and CT. We train a segmentation network in this new representation and employ an additional image-to-image translation network which enables our model to perform on real B-mode images. Quantitative comparisons against fully supervised methods demonstrate the capabilities of CACTUSS in terms of Dice Score and diagnostic metrics, showing that our method also meets the clinical requirements for AAA scanning and diagnosis.

关键词： Ultrasound computer aided intervention Abdominal Aortic Aneurysm Domain Adaptation

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Adversarial Consistency for Single Domain Generalization in medical image Segmentation 25th

Adversarial Consistency for Single Domain Generalization in ...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Xu, Yanwu Xie, Shaoan Reynolds, Maxwell Ragoza, Matthew Gong, Mingming Batmanghelich, Kayhan Univ Pittsburgh Pittsburgh PA 15206 USA Univ Melbourne Sch Math & Stat Melbourne Australia Carnegie Mellon Univ Dept Philosophy Pittsburgh PA USA

ISBN: (纸本)9783031164491;9783031164484

An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training. We propose a novel adversarial domain generalization method for organ segmentation trained on data from a single domain. We synthesize the new domains via learning an adversarial domain synthesizer (ADS) and presume that the synthetic domains cover a large enough area of plausible distributions so that unseen domains can be interpolated from synthetic domains. We propose a mutual information regularizer to enforce the semantic consistency between images from the synthetic domains, which can be estimated by patch-level contrastive learning. We evaluate our method for various organ segmentation for unseen modalities, scanning protocols, and scanner sites.

关键词： medical image segmentation Single domain generalization Adversarial training Mutual information

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