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Less is More: Adaptive Curriculum Learning for thyroid Nodule Diagnosis 25th

Less is More: Adaptive Curriculum Learning for Thyroid Nodul...

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

作者： Gong, Haifan Cheng, Hui Xie, Yifan Tan, Shuangyi Chen, Guanqi Chen, Fei Li, Guanbin Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China Southern Med Univ Zhujiang Hosp Guangzhou Guangdong Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China

ISBN: (纸本)9783031164408;9783031164392

thyroid nodule classification aims at determining whether the nodule is benign or malignant based on a given ultrasound image. However, the label obtained by the cytological biopsy which is the golden standard in clinical medicine is not always consistent with the ultrasound imaging TI-RADS criteria. the information difference between the two causes the existing deep learning-based classification methods to be indecisive. To solve the Inconsistent Label problem, we propose an Adaptive Curriculum Learning (ACL) framework, which adaptively discovers and discards the samples with inconsistent labels. Specifically, ACL takes both hard sample and model certainty into account, and could accurately determine the threshold to distinguish the samples with Inconsistent Label. Moreover, we contribute TNCD: a thyroid Nodule Classification Dataset to facilitate future related research on the thyroid nodules. Extensive experimental results on TNCD based on three different backbone networks not only demonstrate the superiority of our method but also prove that the less-is-more principle which strategically discards the samples with Inconsistent Label could yield performance gains. Source code and data are available at https://***/chenghui-666/ACL/.

关键词： thyroid nodule Adaptive curriculum learning Ultrasound imaging image classification

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RPTK: the Role of Feature Computation on Prediction Performance 26th

RPTK: The Role of Feature Computation on Prediction Performa...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Bohn, Jonas R. Heidt, Christian M. Almeida, Silvia D. Kausch, Lisa Goetz, Michael Nolden, Marco Christopoulos, Petros Rheinheimer, Stephan Peters, Alan A. von Stackelberg, Oyunbileg Kauczor, Hans-Ulrich Maier-Hein, Klaus H. Heussel, Claus P. Norajitra, Tobias German Canc Res Ctr Div Med Image Comp Heidelberg Germany German Ctr Lung Res DZL Translat Lung Res Ctr TLRC Heidelberg Germany Heidelberg Univ Fac Biosci Heidelberg Germany NCT Heidelberg Natl Ctr Tumor Dis NCT Heidelberg Germany Univ Hosp Heidelberg Diagnost & Intervent Radiol Heidelberg Germany Heidelberg Univ Fac Med Heidelberg Germany German Canc Res Ctr AI Hlth Innovat Cluster Heidelberg Germany Univ Ulm Med Ctr Expt Radiol Ulm Germany Univ Hosp Heidelberg Pattern Anal & Learning Grp Heidelberg Germany Thoraxklinik Thorac Oncol Heidelberg Germany Thoraxklinik Diagnost & Intervent Radiol Nucl Med Heidelberg Germany Univ Bern Dept Diagnost Intervent & Pediat Radiol Univ Hosp Bern Bern Switzerland

ISBN: (纸本)9783031474248;9783031474255

A rise of radiomics studies and techniques could be observed over the past few years, which centers around the extraction and analysis of quantitative features from medical images. Radiomics offers numerous advantages in disease characterization and treatment response prediction. Despite its promise, radiomics faces challenges in standardizing features and techniques, leading to large variations of approaches across studies and centers, making it difficult to determine the most suitable techniques for any given clinical scenario. Additionally, manually constructing optimized radiomics pipelines can be time-consuming. Recent works (WORC, Autoradiomics) have addressed the aforementioned shortcomings by introducing radiomics-based frameworks for automated pipeline optimization. Both approaches comprehensively span the entire radiomics workflow, enabling consistent, comprehensive, and reproducible radiomics analyses. In contrast, finding the ideal solutions for the workflow's feature extractor and feature selection components, has received less attention. To address this, we propose the Radiomics Processing Toolkit (RPTK), which adds comprehensive feature extraction and selection components from PyRadiomics and from the medical image Radiomics Processor (MIRP) to the radiomics automation pipeline. To validate our approach and demonstrate benefits from the feature-centered components, we comprehensively compared RPTK with results from WORC and Autoradiomics on six public benchmark data sets. We show that we can achieve higher performance by incorporating the proposed feature processing and selection techniques. Our results provide additional guidance in selecting suitable components for optimized radiomics analyses in clinical use cases such as treatment response prediction.

关键词： Radiomics Evaluation Automated Radiomics Processing Radiomics Processing Toolkit

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S²ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-Supervised Polyp Segmentation 26th

S<SUP>2</SUP>ME: Spatial-Spectral Mutual Teaching and Ensemb...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Wang, An Xu, Mengya Zhang, Yang Islam, Mobarakol Ren, Hongliang Chinese Univ Hong Kong Shun Hing Inst Adv Engn SHIAE Dept Elect Engn Hong Kong Peoples R China Natl Univ Singapore Dept Biomed Engn Singapore Singapore Hubei Univ Technol Sch Mech Engn Wuhan Peoples R China UCL Dept Med Phys & Biomed Engn Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London England

ISBN: (纸本)9783031439063;9783031439070

Fully-supervised polyp segmentation has accomplished significant triumphs over the years in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions from weak supervision like scribbles are rarely explored yet primarily meaningful and demanding in medical practice due to the expensiveness and scarcity of densely-annotated polyp data. Besides, various deployment issues, including data shifts and corruption, put forward further requests for model generalization and robustness. To address these concerns, we design a framework of Spatial-Spectral Dual-branch Mutual Teaching and Entropy-guided Pseudo Label Ensemble Learning (S2ME). Concretely, for the first time in weakly-supervised medical image segmentation, we promote the dual-branch co-teaching framework by leveraging the intrinsic complementarity of features extracted from the spatial and spectral domains and encouraging cross-space consistency through collaborative optimization. Furthermore, to produce reliable mixed pseudo labels, which enhance the effectiveness of ensemble learning, we introduce a novel adaptive pixelwise fusion technique based on the entropy guidance from the spatial and spectral branches. Our strategy efficiently mitigates the deleterious effects of uncertainty and noise present in pseudo labels and surpasses previous alternatives in terms of efficacy. Ultimately, we formulate a holistic optimization objective to learn from the hybrid supervision of scribbles and pseudo labels. Extensive experiments and evaluation on four public datasets demonstrate the superiority of our method regarding in-distribution accuracy, out-of-distribution generalization, and robustness, highlighting its promising clinical significance. Our code is available at https://***/lofrienger/S2ME.

关键词： Polyp image Segmentation Weakly-supervised Learning Spatial-Spectral Dual Branches Mutual Teaching Ensemble Learning

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Learning-Based US-MR Liver image Registration with Spatial Priors 25th

Learning-Based US-MR Liver Image Registration with Spatial P...

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

作者： Zeng, Qi Mohammed, Shahed Pang, Emily H. T. Schneider, Caitlin Honarvar, Mohammad Lobo, Julio Hu, Changhong Jago, James Ng, Gary Rohling, Robert Salcudean, Septimiu E. Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Vancouver Gen Hosp Vancouver BC Canada Philips Healthcare Bothell WA USA Univ British Columbia Dept Mech Engn Vancouver BC Canada

ISBN: (纸本)9783031164460;9783031164453

Registration of multi-modality images is necessary for the assessment of liver disease. In this work, we present an image registration workflow which is designed to achieve reliable alignment for subject-specific magnetic resonance (MR) and intercostal 3D ultrasound (US) images of the liver. Spatial priors modeled from the right rib segmentation are utilized to generate the initial alignment between the MR and US scans without the need of any additional tracking information. For rigid alignment, tissue segmentation models are extracted from the MR and US data with a learning-based approach to apply surface point cloud registration. Local alignment accuracy is further improved via the LC2 image similarity metric-based non-rigid registration technique. this workflow was validated with in vivo liver image data for 18 subjects. the best average thE of rigid and non-rigid registration obtained with our dataset was at 6.27 +/- 2.82 mm and 3.63 +/- 1.87 mm, respectively.

关键词： US-MR image registration Deep learning

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Delving into Local Features for Open-Set Domain Adaptation in Fundus image Analysis 25th

Delving into Local Features for Open-Set Domain Adaptation i...

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

作者： Zhou, Yi Bai, Shaochen Zhou, Tao Zhang, Yu Fu, Huazhu Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China ASTAR Singapore Singapore

ISBN: (纸本)9783031164491;9783031164484

Unsupervised domain adaptation (UDA) has received significant attention in medical image analysis when labels are only available for the source domain data but not for the target domain. Previous UDA methods mainly focused on the closed-set scenario, assuming that only the domain distribution shifts across domains while the label space is the same. However, in practice of medical imaging, the disease categories of training data in source domain are usually limited, and the open-world target domain data may have many 'unknown classes private to the source domain. thus, open-set domain adaptation (OSDA) has great potential in this area. In this paper, we explore the OSDA problem by delving into local features for fundus disease recognition. We propose a collaborative regional clustering and alignment method to identify the common local feature patterns which are category-agnostic. then, a cluster-aware contrastive adaptation loss is introduced to adapt the distributions based on the common local features. We also construct the first fundus image benchmark for OSDA to evaluate our methods and carry out extensive experiments for comparison. It shows that our model achieves consistent improvements over the state-of-the-art methods.

关键词： image analysis

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An Efficient Cross-Modal Segmentation Method for Vestibular Schwannoma and Cochlea on MRI images 1

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Challenge on Brain Tumor Segmentation, BraTS 2023, international Challenge on Cross-Modality Domain Adaptation for medical image Segmentation, CrossMoDA 2023, held in conjunction with the medical image computing for computer assisted intervention conference, MICCAI 2023

作者： Chen, Cancan Wang, Dawei Zhang, Rongguo School of Computer Engineering Jiangsu Ocean University Lianyungang China Infervision Advanced Research Institute Beijing China Academy for Multidisciplinary Studies Capital Normal University Beijing China

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

To obtain the segmentation results of vestibular schwannoma (VS) and cochlea on high-resolution T2 (hrT2) MR images according to the annotated contrast-enhanced T1 (ceT1) MR images, we propose an efficient cross-modal segmentation framework in this study. An image-to-image model is first applied to transfer ceT1 scans to hrT2 modality to alleviate the domain shift between them. In the model training phase, we adopted data augmentation for both original images and segmentation target regions to adapt to the diversity and heterogeneity of multi-center imaging data. Furthermore, random cropping along the z-axis and random flipping at all axial directions representing the different observation perspectives were implemented. We also utilized a 2.5D ResUnet model as the segmentation backbone. these strategies collectively contribute to improved segmentation output. Eventually, a post-processing method based on image contrast is applied to improve the quality of the pseudo-labels on the hrT2 modality. Our experimental results address the effectiveness of the proposed framework for the crossMoDA23 segmentation task of the vestibular schwannoma and cochlea on hrT2 modality, with average Dice scores of 0.8358 and 0.828 for VS and average Dice scores of 0.8355 and 0.844 for cochlea, respectively on validation and test data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image segmentation

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Unsupervised Contrastive Learning of image Representations from Ultrasound Videos with Hard Negative Mining 25th

Unsupervised Contrastive Learning of Image Representations f...

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

作者： Basu, Soumen Singla, Somanshu Gupta, Mayank Rana, Pratyaksha Gupta, Pankaj Arora, Chetan Indian Inst Technol Delhi India Postgrad Inst Med Educ & Res Chandigarh India

ISBN: (纸本)9783031164408;9783031164392

Rich temporal information and variations in viewpoints make video data an attractive choice for learning image representations using unsupervised contrastive learning (UCL) techniques. State-of-the-art (SOTA) contrastive learning techniques consider frames within a video as positives in the embedding space, whereas the frames from other videos are considered negatives. We observe that unlike multiple views of an object in natural scene videos, an Ultrasound (US) video captures different 2D slices of an organ. Hence, there is almost no similarity between the temporally distant frames of even the same US video. In this paper we propose to instead utilize such frames as hard negatives. We advocate mining both intra-video and cross-video negatives in a hardness-sensitive negative mining curriculum in a UCL framework to learn rich image representations. We deploy our framework to learn the representations of Gallbladder (GB) malignancy from US videos. We also construct the first large-scale US video dataset containing 64 videos and 15,800 frames for learning GB representations. We show that the standard ResNet50 backbone trained with our framework improves the accuracy of models pretrained with SOTA UCL techniques as well as supervised pretrained models on imageNet for the GB malignancy detection task by 2-6%. We further validate the generalizability of our method on a publicly available lung US image dataset of COVID-19 pathologies and show an improvement of 1.5% compared to SOTA. Source code, dataset, and models are available at https://***/usucl.

关键词： Contrastive learning Ultrasound Negative mining

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XMorpher: Full Transformer for Deformable medical image Registration via Cross Attention 25th

XMorpher: Full Transformer for Deformable Medical Image Regi...

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

作者： Shi, Jiacheng He, Yuting Kong, Youyong Coatrieux, Jean-Louis Shu, Huazhong Yang, Guanyu Li, Shuo Southeast Univ Key Lab Comp Network & Informat Integrat LIST Minist Educ Nanjing Peoples R China Jiangsu Prov Joint Int Res Lab Med Informat Proc Nanjing Peoples R China Ctr Rech Informat Biomed Sino Francais CRIBs Rennes France Univ Western Ontario Dept Med Biophys London ON Canada

ISBN: (纸本)9783031164460;9783031164453

An effective backbone network is important to deep learning-based Deformable medical image Registration (DMIR), because it extracts and matches the features between two images to discover the mutual correspondence for fine registration. However, the existing deep networks focus on single image situation and are limited in registration task which is performed on paired images. therefore, we advance a novel backbone network, XMorpher, for the effective corresponding feature representation in DMIR. 1) It proposes a novel full transformer architecture including dual parallel feature extraction networks which exchange information through cross attention, thus discovering multi-level semantic correspondence while extracting respective features gradually for final effective registration. 2) It advances the Cross Attention Transformer (CAT) blocks to establish the attention mechanism between images which is able to find the correspondence automatically and prompts the features to fuse efficiently in the network. 3) It constrains the attention computation between base windows and searching windows with different sizes, and thus focuses on the local transformation of deformable registration and enhances the computing efficiency at the same time. Without any bells and whistles, our XMorpher gives Voxelmorph 2.8% improvement on DSC, demonstrating its effective representation of the features from the paired images in DMIR. We believe that our XMorpher has great application potential in more paired medical images. Our XMorpher is open on https://***/Solemoon/XMorpher

关键词： image enhancement

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Self-supervised 3D Anatomy Segmentation Using Self-distilled Masked image Transformer (SMIT) 25th

Self-supervised 3D Anatomy Segmentation Using Self-distilled...

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

作者： Jiang, Jue Tyagi, Neelam Tringale, Kathryn Crane, Christopher Veeraraghavan, Harini Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10021 USA Mem Sloan Kettering Canc Ctr Dept Radiat Oncol 1275 York Ave New York NY 10021 USA

ISBN: (纸本)9783031164408;9783031164392

Vision transformers efficiently model long-range context and thus have demonstrated impressive accuracy gains in several image analysis tasks including segmentation. However, such methods need large labeled datasets for training, which is hard to obtain for medical image analysis. Self-supervised learning (SSL) has demonstrated success in medical image segmentation using convolutional networks. In this work, we developed a self-distillation learning with masked image modeling method to perform SSL for vision transformers (SMIT) applied to 3D multi-organ segmentation from CT and MRI. Our contribution combines a dense pixel-wise regression pretext task performed within masked patches called masked image prediction with masked patch token distillation to pre-train vision transformers. Our approach is more accurate and requires fewer fine tuning datasets than other pretext tasks. Unlike prior methods, which typically used image sets arising from disease sites and imaging modalities corresponding to the target tasks, we used 3,643 CT scans (602,708 images) arising from head and neck, lung, and kidney cancers as well as COVID-19 for pre-training and applied it to abdominal organs segmentation from MRI pancreatic cancer patients as well as publicly available 13 different abdominal organs segmentation from CT. Our method showed clear accuracy improvement (average DSC of 0.875 from MRI and 0.878 from CT) with reduced requirement for fine-tuning datasets over commonly used pretext tasks. Extensive comparisons against multiple current SSL methods were done. Our code is available at: https://***/harveerar/***.

关键词： Self-supervised learning Segmentation Self-distillation Masked image modeling Masked embedding transformer

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An End-to-End Combinatorial Optimization Method for R-band Chromosome Recognition with Grouping Guided Attention 25th

An End-to-End Combinatorial Optimization Method for R-band C...

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

作者： Xia, Chao Wang, Jiyue Qin, Yulei Gu, Yun Chen, Bing Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Med Shanghai Inst Hematol Ruijin Hosp Shanghai Peoples R China

ISBN: (纸本)9783031164408;9783031164392

Chromosome recognition is a critical and time-consuming process in karyotyping, especially for R-band chromosomes with poor visualization quality. Existing computer-aided chromosome recognition methods mainly focus on better feature representation of individual chromosomes while neglecting the fact that chromosomes from the same karyotype share some common distribution and are more related compared to chromosomes across different patients. In the light of such observation, we start from a global perspective and propose an end-to-end differential combinatorial optimization method for R-band chromosome recognition. To achieve this, a grouping guided feature interaction module (GFIM) is built for feature aggregation between similar chromosome instances. Specially, a mask matrix is built for self-attention computation according to chromosome length grouping information. Furthermore, a deep assignment module (DAM) is designed for flexible and differentiable label assignment. It exploits the aggregated features to infer class probability distributions between all chromosomes in a cell. Experimental results on both normal and numerically abnormal karyotypes confirmed that our method outperforms state-of-the-art chromosome recognition and label assignment methods. the code is available at: https://***/xiabc612/R-band-chromosome-recognition.

关键词： R-band chromosome Label assignment Transformer

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