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Multi-task Framework for Myocardial Pathological Segmentation on Multi-sequence Cardiac Magnetic Resonance 1st

Multi-task Framework for Myocardial Pathological Segmentati...

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1st MICCAI Challenge Comprehensive Analysis and computing of Real-World medical images, CARE 2024 Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Wu, Yilin Yang, Xiao Liu, Yongli Zheng, Jiahao Yang, Mingjing College of Physics and Information Engineering Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783031870088

Accurate segmentation of myocardial pathology, including scar and edema, from magnetic resonance images (MRI) is of significant clinical importance for diagnosing, planning treatment, and assessing prognosis in cardiovascular diseases. Multi-sequence cardiac magnetic resonance images provide additional information for pathological segmentation, while ventricular blood pool regions offer positional anatomical priors for identifying scar and edema. therefore, we propose a multi-task framework based on SimAM-UNet, where SimAM generates feature weights to better focus on modality-specific features for the segmentation of myocardial pathology using multi-sequence cardiac magnetic resonance images. the first task involves segmenting and extracting features of the myocardium and blood pool, which are relatively easy to obtain. the second task focuses on segmenting scars and edema by integrating positional feature constraints derived from the myocardium and blood pool. To address the multi-center data problem encountered in real-world data, we incorporate a data augmentation pipeline that simulates the generation of data samples from different centers, enhancing the domain generalization of our method. Our proposed method was evaluated on the MyoPS++ track of the CARE2024 Challenge, and the experimental results demonstrated the effectiveness of our multi-task framework for myocardial pathological segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Diabetic Foot Ulcer Grand Challenge 2024: Overview and Baseline Methods 4th

Diabetic Foot Ulcer Grand Challenge 2024: Overview and Base...

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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： Brüngel, Raphael Kendrick, Connah Cassidy, Bill Bracke, Benjamin Friedrich, Christoph M. Reeves, Neil D. Pappachan, Joseph M. Yap, Moi Hoon Dortmund44227 Germany University Hospital Essen Essen45130 Germany University Hospital Essen Essen45131 Germany Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Lancaster University LancasterLA1 4YW United Kingdom Lancashire Teaching Hospitals NHS Foundation Trust PrestonPR2 9HT United Kingdom

ISBN: (纸本)9783031808708

In its 4th edition, the Diabetic Foot Ulcer Challenge (DFUC) 2024 focuses on the task of Diabetic Foot Ulcer (DFU) segmentation by involving aspects of self-supervised learning as contrast to the DFUC 2022. this paper provides information on the challenge background, outlines details on an synthetic extension of the DFU dataset, elaborates rules for participation, and reports performance of two vastly different baseline methods that highlight the potential diversity of welcome approaches. It further provides a condensed summary on final challenge results and methods employed by participating teams. A discussion puts the different approaches and outcomes into perspective, considering their creativity and applicability on a larger scale. Eventually, a retrospective on the DFUC 2024 finds the given task to be considerably challenging, inspiring diverse contributions. Even though accurate DFU segmentation currently seems out of reach when involving a higher level of self-supervision, respective contributions demonstrate promising kick-off strategies for scenarios in which unlabelled data needs to be harnessed. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Embedding Gradient-Based Optimization in image Registration Networks 25th

Embedding Gradient-Based Optimization in Image Registration ...

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

作者： Qiu, Huaqi Hammernik, Kerstin Qin, Chen Chen, Chen Rueckert, Daniel Imperial Coll London BioMedIA Grp London England Tech Univ Munich Klinikum Rechts Isar Munich Germany Univ Edinburgh Inst Digital Commun Edinburgh Scotland

ISBN: (纸本)9783031164460;9783031164453

Deep learning (DL) image registration methods amortize the costly pair-wise iterative optimization by training deep neural networks to predict the optimal transformation in one fast forward-pass. In this work, we bridge the gap between traditional iterative energy optimization-based registration and network-based registration, and propose Gradient Descent Network for image Registration (GraDIRN). Our proposed approach trains a DL network that embeds unrolled multi-resolution gradient-based energy optimization in its forward pass, which explicitly enforces image dissimilarity minimization in its update steps. Extensive evaluations were performed on registration tasks using 2D cardiac MR and 3D brain MR images. We demonstrate that our approach achieved state-of-the-art registration performance while using fewer learned parameters, with good data efficiency and domain robustness.

关键词： medical image registration Deep learning Unrolled optimization network

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image Captioning with Multiple Perspectives—A Visual Context-Based Approach 5th

Image Captioning with Multiple Perspectives—A Visual Contex...

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5th international conference on computing and Network Communications, CoCoNet 2023

作者： Ashwin, G. Chaitanya, V. Kishan Rohith Nair, Priyanka C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (纸本)9789819747108

this study tackles the difficult issues of image captioning while negotiating the complexity of visual data processing. the complexity of visual data and the associated processing requirements make image captioning a daunting task. this research describes an effective method for image captioning that makes advantage of attentional mechanisms. Several models, including Transformer, VGG16, VGG19, Inception, RNN decoders, and Bahdanau’s attention mechanism, are contrasted and analyzed to demonstrate the benefits of integrating attention processes. By allowing the model to focus on the appropriate visual component, attention improves the accuracy of the generated captions. Transformer models, in particular, outperform other methods by capturing complicated relationships and delivering accurate output, and they have the highest BLEU Score of 70. the findings emphasize the significance of attention mechanisms and the relevance of selecting a suitable model architecture to maximize picture captioning performance. the variety of potential applications emphasizes the benefits and potential impact of image captioning in a broad range of scenarios. Several use cases are found beneficial, including supporting the visually impaired, improving product descriptions in e-commerce, assisting medical diagnosis, improving image search ability, and allowing effective communication and comprehension of visual information. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Regression Metric Loss: Learning a Semantic Representation Space for medical images 25th

Regression Metric Loss: Learning a Semantic Representation S...

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

作者： Chao, Hanqing Zhang, Jiajin Yan, Pingkun Rensselaer Polytech Inst Dept Biomed Engn Ctr Biotechnol & Interdisciplinary Studies Troy NY 12180 USA

ISBN: (纸本)9783031164521;9783031164514

Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores. While training strategies and loss functions have been studied for the deep neural networks in medical image classification tasks, options for regression tasks are very limited. One of the key challenges is that the high-dimensional feature representation learned by existing popular loss functions like Mean Squared Error or L1 loss is hard to interpret. In this paper, we propose a novel Regression Metric Loss (RM-Loss), which endows the representation space with the semantic meaning of the label space by finding a representation manifold that is isometric to the label space. Experiments on two regression tasks, i.e. coronary artery calcium score estimation and bone age assessment, show that RM-Loss is superior to the existing popular regression losses on both performance and interpretability. Code is available at https://github. com/DIAL-RPI/Regression-Metric-Loss.

关键词： medical image regression Metric learning Representation learning Semantic representation Interpretability

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DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide image Classification 25th

DGMIL: Distribution Guided Multiple Instance Learning for Wh...

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

作者： Qu, Linhao Luo, Xiaoyuan Liu, Shaolei Wang, Manning Song, Zhijian Fudan Univ Shanghai Key Lab Med Image Comp & Comp Assisted I Sch Basic Med Sci Digital Med Res Ctr Shanghai 200032 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide images (WSIs). However, existing MIL methods do not explicitly model the data distribution, and instead they only learn a bag-level or instance-level decision boundary discriminatively by training a classifier. In this paper, we propose DGMIL: a feature distribution guided deep MIL framework for WSI classification and positive patch localization. Instead of designing complex discriminative network architectures, we reveal that the inherent feature distribution of histopathological image data can serve as a very effective guide for instance classification. We propose a cluster-conditioned feature distribution modeling method and a pseudo label-based iterative feature space refinement strategy so that in the final feature space the positive and negative instances can be easily separated. Experiments on the CAME-LYON16 dataset and the TCGA Lung Cancer dataset show that our method achieves new SOTA for both global classification and positive patch localization tasks.

关键词： Histopathological images Multiple Instance Learning

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Exploring CNN and Transformer-Based Architectures to Improve image Segmentation for Chronic Wound Measurement 3rd

Exploring CNN and Transformer-Based Architectures to Impro...

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3rd international Workshop on Applications of medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Carvalho, Rafaela Morgado, Ana C. Sampaio, Ana Filipa Vasconcelos, Maria J. M. Fraunhofer Portugal AICOS Porto Portugal

ISBN: (纸本)9783031820069

the high prevalence of chronic wounds and their consequences for people’s quality of life makes wound treatment a highly relevant topic in the context of healthcare. One vital aspect of monitoring concerns tracking wound size evolution, which guides healthcare professionals during diagnosis and serves as a key predictor of treatment efficacy. this work proposes an automatic image segmentation and measurement framework for chronic wounds using deep learning and computer vision techniques. the wound segmentation task involved exploring three prominent segmentation models: a popular convolutional neural network (DeepLabV3+), a cutting-edge transformer approach (SegFormer) and a visual foundation model (MedSAM). Traditional computer vision techniques were further applied to infer the open wound’s width, length and area in real-world units during the wound measurement task. Separate studies were performed to assess each task’s performance and a final assessment of the complete framework that couples a wound and reference marker detection model with the developed segmentation and measurement approach. For the automatic wound segmentation, MedSAM achieved the best performance with Dice scores of 88.14% and 92.25%, applied on public AZH FU and private datasets, respectively. In the wound measurement task, the area estimation achieved a mean relative error of 5.36% for the private dataset. Concerning the overall pipeline results, MedSAM experienced a decline in performance and SegFormer emerged as the best segmentation model, achieving a Dice score of 91.55% and a 16.7% mean relative error for the area estimation (which surpasses the literature results) in the private dataset, demonstrating its applicability in clinical practice. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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CRISP - Reliable Uncertainty Estimation for medical image Segmentation 25th

CRISP - Reliable Uncertainty Estimation for Medical Image Se...

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

作者： Judge, thierry Bernard, Olivier Porumb, Mihaela Chartsias, Agisilaos Beqiri, Arian Jodoin, Pierre-Marc Univ Sherbrooke Dept Comp Sci Sherbrooke PQ Canada Ultromics Ltd Oxford OX4 2SU England Univ Lyon Univ Lyon 1 INSA Lyon CREATISCNRS UMR5220Inserm U1294 Villeurbanne France

ISBN: (纸本)9783031164521;9783031164514

Accurate uncertainty estimation is a critical need for the medical imaging community. A variety of methods have been proposed, all direct extensions of classification uncertainty estimations techniques. the independent pixel-wise uncertainty estimates, often based on the probabilistic interpretation of neural networks, do not take into account anatomical prior knowledge and consequently provide sub-optimal results to many segmentation tasks. For this reason, we propose CRISP a ContRastive image Segmentation for uncertainty Prediction method. At its core, CRISP implements a contrastive method to learn a joint latent space which encodes a distribution of valid segmentations and their corresponding images. We use this joint latent space to compare predictions to thousands of latent vectors and provide anatomically consistent uncertainty maps. Comprehensive studies performed on four medical image databases involving different modalities and organs underlines the superiority of our method compared to state-of-the-art approaches. Code is available at: https://***/thierryJudge/CRISP-uncertainty.

关键词： medical imaging Segmentation Uncertainty Deep learning

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Lossless image Compression using K-Means Clustering in Color Pixel Domain 5

Lossless Image Compression using K-Means Clustering in Color...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumari, Rimjhim Sriramulu, Srinivasan Galgotias University Department of Computer Science and Engineering Greater Noida India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Compressing images is a method for shrinking the image's dimensions using a particular algorithm. image compression is a solution associated with transmitting and storing large amounts of data for digital images. While image storage is necessary for medical images, satellite images, documents, and pictures, image transmission includes a variety of applications like television broadcasting, remote sensing via satellite, and other long-distance communication. these kinds of applications deal with image compression. Lossy compression and lossless compression are two separate methods for compressing images. While unneeded metadata is deleted in lossless compression to minimize file size, lossy compression permanently removes part of the original data to reduce file size. the terms 'supervised learning' and 'unsupervised learning' refer to two different machine learning techniques. As opposed to a non-supervised learning algorithm, which uses unlabeled input data, supervised learning uses labeled data. While clustering is a subtype of unsupervised learning, classification, and regression are additional subcategories of supervised learning. this paper discusses the application of clustering using K-means. this methodology explores the concept of lossless picture compression is the process of reducing the size of an image without sacrificing its quality which is achieved using k-mean clustering. the proposed methodology achieves higher compression ratios than other existing approaches to image compression algorithms. © 2024 IEEE.

关键词： clustering compression image k-mean lossless unsupervised

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ProCo: Prototype-Aware Contrastive Learning for Long-Tailed medical image Classification 25th

ProCo: Prototype-Aware Contrastive Learning for Long-Tailed ...

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

作者： Yang, Zhixiong Pan, Junwen Yang, Yanzhan Shi, Xiaozhou Zhou, Hong-Yu Zhang, Zhicheng Bian, Cheng Xiaohe Healthcare ByteDance Guangzhou Peoples R China Univ Hong Kong Dept Comp Sci Pokfulam Hong Kong Peoples R China

ISBN: (纸本)9783031164521;9783031164514

medical image classification has been widely adopted in medical image analysis. However, due to the difficulty of collecting and labeling data in the medical area, medical image datasets are usually highly-imbalanced. To address this problem, previous works utilized class samples as prior for re-weighting or re-sampling but the feature representation is usually still not discriminative enough. In this paper, we adopt the contrastive learning to tackle the long-tailed medical imbalance problem. Specifically, we first propose the category prototype and adversarial proto-instance to generate representative contrastive pairs. then, the prototype recalibration strategy is proposed to address the highly imbalanced data distribution. Finally, a unified proto-loss is designed to train our framework. the overall framework, namely as Prototype-aware Contrastive learning (ProCo), is unified as a single-stage pipeline in an end-to-end manner to alleviate the imbalanced problem in medical image classification, which is also a distinct progress than existing works as they follow the traditional two-stage pipeline. Extensive experiments on two highly-imbalanced medical image classification datasets demonstrate that our method outperforms the existing state-of-the-art methods by a large margin. Our source codes are available at https://***/skyz215/ProCo.

关键词： Contrastive learning Prototype Imbalanced dataset

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