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ACTION++: Improving Semi-supervised medical image Segmentation with Adaptive Anatomical Contrast 26th

ACTION++: Improving Semi-supervised Medical Image Segmentati...

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26th international conference on medical image computing and computer-assisted intervention, MICCAI 2023

作者： You, Chenyu Dai, Weicheng Min, Yifei Staib, Lawrence Sekhon, Jas Duncan, James S. Department of Electrical Engineering Yale University New Haven United States Department of Radiology and Biomedical Imaging Yale University New Haven United States Department of Biomedical Engineering Yale University New Haven United States Department of Statistics and Data Science Yale University New Haven United States Department of Political Science Yale University New Haven United States

ISBN: (纸本)9783031439001

medical data often exhibits long-tail distributions with heavy class imbalance, which naturally leads to difficulty in classifying the minority classes (i.e., boundary regions or rare objects). Recent work has significantly improved semi-supervised medical image segmentation in long-tailed scenarios by equipping them with unsupervised contrastive criteria. However, it remains unclear how well they will perform in the labeled portion of data where class distribution is also highly imbalanced. In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation. Specifically, we propose an adaptive supervised contrastive loss, where we first compute the optimal locations of class centers uniformly distributed on the embedding space (i.e., off-line), and then perform online contrastive matching training by encouraging different class features to adaptively match these distinct and uniformly distributed class centers. Moreover, we argue that blindly adopting a constant temperature $$\tau $$ in the contrastive loss on long-tailed medical data is not optimal, and propose to use a dynamic $$\tau $$ via a simple cosine schedule to yield better separation between majority and minority classes. Empirically, we evaluate ACTION++ on ACDC and LA benchmarks and show that it achieves state-of-the-art across two semi-supervised settings. theoretically, we analyze the performance of adaptive anatomical contrast and confirm its superiority in label efficiency. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image enhancement

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RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level image Quality Assessment 25th

RTN: Reinforced Transformer Network for Coronary CT Angiogra...

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

作者： Lu, Yiting Fu, Jun Li, Xin Zhou, Wei Liu, Sen Zhang, Xinxin wu, Wei Jia, Congfu Liu, Ying Chen, Zhibo Univ Sci & Technol China Hefei Anhui Peoples R China Dalian Med Univ Affiliated Hosp 1 Dalian Liaoning Peoples R China

ISBN: (纸本)9783031164316;9783031164309

Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. the appropriate CCTA Vessel-level image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. the primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL module (termed as T-MIL) to aggregate the multiple instances along the coronary centerline into the final quality. However, not all instances are informative for final quality. there are some quality-irrelevant/negative instances intervening the exact quality assessment(e.g., instances covering only background or the coronary in instances is not identifiable). therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA. Based on the above two modules, we propose a Reinforced Transformer Network (RTN) for automatic CCTA VIQA based on endto-end optimization. the experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the real-world CCTA dataset, exceeding previous MIL methods by a large margin.

关键词： image quality assessment CCTA Reinforced learning Transformer

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Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide images Analysis 25th

Lesion-Aware Contrastive Representation Learning for Histopa...

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

作者： Li, Jun Zheng, Yushan Wu, Kun Shi, Jun Xie, Fengying Jiang, Zhiguo Beihang Univ Sch Astronaut Image Proc Ctr Beijing 102206 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Engn Med Beijing 100191 Peoples R China Hefei Univ Technol Sch Software Hefei 230601 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

image representation learning has been a key challenge to promote the performance of the histopathological whole slide images analysis. the previous representation learning methods followed the supervised learning paradigm. However, manual annotation for large-scale WSIs is time-consuming and labor-intensive. Hence, the self-supervised contrastive learning has recently attracted intensive attention. In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis. We built a lesion queue based on the memory bank structure to store the representations of different classes of WSIs, which allowed the contrastive model to selectively define the negative pairs during the training. Moreover, We designed a queue refinement strategy to purify the representations stored in the lesion queue. the experimental results demonstrate that LACL achieves the best performance in histopathology image representation learning on different datasets, and outperforms state-of-the-art methods under different WSI classification benchmarks.

关键词： Weakly supervised learning Contrastive learning WSI analysis

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UASSR: Unsupervised Arbitrary Scale Super-Resolution Reconstruction of Single Anisotropic 3D images via Disentangled Representation Learning 25th

UASSR: Unsupervised Arbitrary Scale Super-Resolution Reconst...

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

作者： Wang, Jiale Wang, Runze Tao, Rong Zheng, Guoyan Shanghai Jiao Tong Univ Inst Med Robot Sch Biomed Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Deep learning-based single image super resolution (SISR) algorithms have great potential to recover high-resolution (HR) images from low-resolution (LR) inputs. However, most studies require paired LR and HR images for a supervised training, which are difficult to organize in clinical applications. In this paper, we propose an unsupervised arbitrary scale super-resolution reconstruction (UASSR) method based on disentangled representation learning, eliminating the requirement of paired images for training. Applying our method to applications of generating HR images with high inter-plane resolution from LR images with low inter-plane resolution. At the inference stage, we design a strategy to fuse multiple reconstructed HR images from different views to achieve better super-resolution (SR) result. We conduct experiments on one publicly available dataset including 507 MR volumes of the knee joint and an in-house dataset containing 130 CT volumes of the lower spine. Results from our comprehensive experiments demonstrate superior performance of UASSR over other state-of-the-art methods. A reference implementation of our method can be found at: https://***/jialewang1/UASSR.

关键词： Unsupervised image super-resolution Arbitrary scale Disentangled representation learning Magnetic resonance imaging Computed tomography

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the Dice Loss in the Context of Missing or Empty Labels: Introducing Φ and ε 25th

The Dice Loss in the Context of Missing or Empty Labels: Int...

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

作者： Tilborghs, Sofie Bertels, Jeroen Robben, David Vandermeulen, Dirk Maes, Frederik Katholieke Univ Leuven ESAT PSI Dept Elect Engn Leuven Belgium UZ Leuven Med Imaging Res Ctr Leuven Belgium Icometrix Kolonel Begaultlaan Ib-12 Leuven Belgium

ISBN: (纸本)9783031164439;9783031164422

Albeit the Dice loss is one of the dominant loss functions in medical image segmentation, most research omits a closer look at its derivative, i.e. the real motor of the optimization when using gradient descent. In this paper, we highlight the peculiar action of the Dice loss in the presence of missing or empty labels. First, we formulate a theoretical basis that gives a general description of the Dice loss and its derivative. It turns out that the choice of the reduction dimensions Phi and the smoothing term epsilon is non-trivial and greatly influences its behavior. We find and propose heuristic combinations of Phi and e that work in a segmentation setting with either missing or empty labels. Second, we empirically validate these findings in a binary and multiclass segmentation setting using two publicly available datasets. We confirm that the choice of Phi and is indeed pivotal. With Phi chosen such that the reductions happen over a single batch (and class) element and with a negligible epsilon, the Dice loss deals with missing labels naturally and performs similarly compared to recent adaptations specific for missing labels. With Phi chosen such that the reductions happen over multiple batch elements or with a heuristic value for epsilon, the Dice loss handles empty labels correctly. We believe that this work highlights some essential perspectives and hope that it encourages researchers to better describe their exact implementation of the Dice loss in future work.

关键词： image segmentation

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Semi-supervised Spatial Temporal Attention Network for Video Polyp Segmentation 1

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

作者： Zhao, Xinkai Wu, Zhenhua Tan, Shuangyi Fan, De-Jun Li, Zhen Wan, Xiang Li, Guanbin Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China Sun Yat Sen Univ Affiliated Hosp 6 Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Deep learning-based polyp segmentation approaches have achieved great success in image datasets. However, the frame-by-frame annotation of polyp videos requires a large amount of workload, which limits the application of polyp segmentation algorithms in clinical videos. In this paper, we address the semi-supervised video polyp segmentation task, which requires only sparsely annotated frames to train a video polyp segmentation network. We propose a novel spatial-temporal attention network which is composed of Temporal Local Context Attention (TLCA) module and Proximity Frame Time-Space Attention (PFTSA) module. Specifically, TLCA module is to refine the prediction of the current frame using the prediction results of the nearby frames in the video clip. PFTSA module utilizes a simple yet powerful hybrid transformer architecture to capture long-range dependencies in time and space efficiently. Combined with consistency constraints, the network fuses representations of proximity frames at different scales to generate pseudo-masks for unlabeled images. We further propose a pseudomask-based training method. Additionally, we re-masked a subset of LDPolypVideo and applied it as a semi-supervised polyp segmentation dataset for our experiments. Experimental results show that our proposed semi-supervised approach can outperform existing image-level semi-supervised and fully supervised methods with sparse annotation at a speed of 135 fps. the code is available at ***/ShinkaiZ/SSTAN.

关键词： Polyp segmentation Semi-supervised learning medical image segmentation

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Transformer Based Multiple Instance Learning for Weakly Supervised Histopathology image Segmentation 25th

Transformer Based Multiple Instance Learning for Weakly Supe...

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

作者： Qian, Ziniu Li, Kailu Lai, Maode Chang, Eric I-Chao Wei, Bingzheng Fan, Yubo Xu, Yan Beihang Univ State Key Lab Software Dev Environm Key Lab Biomechan Mechanobiol Minist Educ Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Biol Sci & Med Engn Beijing 100191 Peoples R China China Pharmaceut Univ Nanjing 210009 Peoples R China Zhejiang Univ Hangzhou 310058 Peoples R China Microsoft Res Beijing 100080 Peoples R China Xiaomi Corp Beijing 100085 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology. the development of weakly supervised segmentation algorithm alleviates the problem of medical image annotation that it is time-consuming and labor-intensive. As a subset of weakly supervised learning, Multiple Instance Learning (MIL) has been proven to be effective in segmentation. However, there is a lack of related information between instances in MIL, which limits the further improvement of segmentation performance. In this paper, we propose a novel weakly supervised method for pixel-level segmentation in histopathology images, which introduces Transformer into the MIL framework to capture global or long-range dependencies. the multi-head self-attention in the Transformer establishes the relationship between instances, which solves the shortcoming that instances are independent of each other in MIL. In addition, deep supervision is introduced to overcome the limitation of annotations in weakly supervised methods and make the better utilization of hierarchical information. the state-of-the-art results on the colon cancer dataset demonstrate the superiority of the proposed method compared with other weakly supervised methods. It is worth believing that there is a potential of our approach for various applications in medical images.

关键词： Weakly supervised learning Transformer Multiple Instance Learning Segmentation

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Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation 4th

Translating Clinical Delineation of Diabetic Foot Ulcers in...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Kendrick, Connah Cassidy, Bill Pappachan, Joseph M. O’Shea, Claire Fernandez, Cornelious J. Chacko, Elias Jacob, Koshy Reeves, Neil D. Yap, Moi Hoon Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Lancashire Teaching Hospitals NHS Foundation Trust PrestonPR2 9HT United Kingdom Waikato District Health Board Hamilton3240 New Zealand United Lincolnshire Hospitals NHS Trust LincolnshireLN2 5QY United Kingdom Jersey General Hospital JerseyJE1 3QS United Kingdom Eastbourne District General Hospital East Sussex EastbourneBN21 2UD United Kingdom Lancaster University LancasterLA1 4YW United Kingdom

ISBN: (纸本)9783031808708

Diabetic foot ulcer (DFU) is a severe condition that requires close monitoring and management. Experienced clinical staff are best-positioned to provide ground truth annotations to train machine learning methods to auto-delineate the ulcer. In this paper, we revisit the largest DFU dataset for segmentation that was used in the Diabetic Foot Ulcer Grand Challenge 2022 (DFUC 2022) and provide further analysis. the DFUC 2022 segmentation dataset provides clinical expert delineation of ulcer regions. We assess whether the clinical delineations are easily machine interpretable by deep learning networks or if image-processing refined contours should be used. this paper demonstrates that image processing using refined delineations as ground truth can provide better agreement with machine-predicted results. With the in-depth understanding and observation of baseline models, we provide insights to guide future development in DFU segmentation. this dataset continues to serve as the challenge dataset for DFUC 2024, and it is available at: https://***/. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Uncertainty-aware Cascade Network for Ultrasound image Segmentation with Ambiguous Boundary 1

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

作者： Xie, Yanting Liao, Hongen Zhang, Daoqiang Chen, Fang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing 211106 Peoples R China Tsinghua Univ Sch Med Dept Biomed Engn Beijing 10084 Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Ultrasound image segmentation plays an essential role in automatic disease diagnosis. However, to achieve precise ultrasound segmentation is still a challenge caused by the ambiguous lesion boundary and imaging artifacts such as speckles and shadowing noise. Considering that the pixels with high uncertainty generally distributing in the boundary regions of prediction maps, are likely to overlap with the confused regions of ultrasound, we proposed an uncertainty-aware cascade network. Our network uses the confidence map to evaluate the uncertainty of each pixel to enhance the segmentation of ambiguous boundary. On the one hand, the confidence map fuses with the ultrasound features and predicted mask using the adaptive fusion module (AFM) which enriches the context features from different modalities. In addition, the uncertainty attention module (UAM) is proposed based on the confidence map. this module focuses on the influential features with cross attention constrained by the uncertainty of pixels which can extract the localized features of confused ultrasound regions. On the other hand, the recurrent edge correction module (RECM) further improves the segmentation of ambiguous boundary. this module increases the weights of confident features neighboring the uncertainty boundaries in order to refine the predictions of edge pixels with low confidence. We evaluated the proposed method on three public ultrasound datasets and the segmentation results show that our method achieved higher Dice scores and lower Hausdorff distance with more precise boundary details compared with state-of-the-art methods.

关键词： Uncertainty-aware Segmentation Ultrasound Ambiguous boundary Confidence map

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LogTrans: Providing Efficient Local-Global Fusion with Transformer and CNN Parallel Network for Biomedical image Segmentation 24

LogTrans: Providing Efficient Local-Global Fusion with Trans...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Nie, Xingqing Zhou, Xiaogen Li, Zhiqiang Wang, Luoyan Lin, Xingtao Tong, Tong College of Physics and Information Engineering Fuzhou University Fuzhou China Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China

ISBN: (纸本)9798350319934

Accurate biomedical image segmentation is a prerequisite for excellent computer-aided diagnosis (CAD) systems. A series of researches have shown that convolutional neural networks (CNNs) have made impressive progress in segmentation tasks. Nevertheless, owing to the finite receptive field of CNN-based algorithms, such networks are focused too much on local area features rather than global context. While the Transformer architecture can encode global dependencies information through the self-attention mechanism, this mechanism typically ignores the local pixel-level structural information within each divided patch. therefore, a better solution is still needed for how to integrate CNN architecture with Transformer architecture efficiently. In this essay, we propose an originative parallel segmentation algorithm called LogTrans. First, in the encoder path, the local details and global contour dependencies on the entire image are captured by the CNN branch and the Transformer branch, respectively. then these two branches complement each other by a novel separate-combiner (SeCo) module, leading to better fused features. Moreover, we attempt to further enhance the segmentation properties by using a residual stackable dilated (ReSD) block, which applies residual shortcut connections to resolve dimension alterations in the target region and stacks dilated convolutions to capture more spatial information. the proposed LogTrans framework was evaluated on two biomedical datasets, including ISIC-2017 and UITNS-2022 datasets. Collectively, multiple results have indicated that our LogTrans performs superior with other state-of-the-art architectures in both visual comparison and quantitative appraisal. © 2022 IEEE.

关键词： Convolutional neural networks

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