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Synthesising Brain Iron Maps from Quantitative Magnetic Resonance images Using Interpretable Generative Adversarial Networks 26th

Synthesising Brain Iron Maps from Quantitative Magnetic Reso...

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

作者： Munroe, Lindsay Deprez, Maria Michaelides, Christos Parkes, Harry G. Geraki, Kalotina Herlihy, Amy H. So, Po-Wah Kings Coll London Dept Neuroimaging Inst Psychiat Psychol & Neurosci London England Kings Coll London Sch Biomed Engn & Imaging Sci London England Diamond Light Source Harwell Sci & Innovat Campus Didcot Oxon England Perspectum Diagnost Gemini One5520 John Smith Dr Oxford England

ISBN: (纸本)9783031474248;9783031474255

Accurate spatial estimation of brain iron concentration in-vivo is vital to elucidate the role of iron in neurodegenerative diseases, among other applications. However, ground truth quantitative iron maps of the brain can only be acquired post-mortem from ex-vivo samples. Quantitative magnetic resonance imaging (QMRI) methods are iron-sensitive and hold potential to quantitatively measure brain iron. We hypothesise interpretability methods can identify the most salient QMRI parameter(s) for iron prediction. In this study, a generative adversarial network with spatially adaptive normalisation layers (SPADE) was trained to synthesise maps of brain iron content from QMRI parameters, including those from relaxometry, diffusion and magnetisation transfer MRI. Ground truth maps of iron content were obtained by synchrotron radiation X-ray fluorescence (SRXRF). QMRI and SRXRF datasets were registered, and a distribution-based loss was proposed to address misalignment from multi-modal QMRI-to-SRXRF registration. To enable interpretation, channel attention was incorporated to learn feature importance for QMRI parameters. Attention weights were compared against occlusion and local interpretable model-agnostic explanations. Our model achieved dice scores of 0.97 and 0.95 for grey and white matter, respectively, when comparing tissue boundaries of synthesised vs. MRI images. Examining the contrast in predicted vs. ground truth iron maps, our model achieved 15.2% and 17.8% normalised absolute error for grey and white matter, respectively. All three interpretable methods ranked fractional anisotropy as the most salient, followed by myelin water fraction and magnetisation transfer ratio. the co-location of iron and myelin may explain the finding that myelin-related QMRI parameters are strong predictors of iron.

关键词： Supervised image-to-image translation Interpretable deep learning Iron prediction

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A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000 25th

A Comprehensive Study of Modern Architectures and Regulariza...

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

作者： Ihler, Sontje Kuhnke, Felix Spindeldreier, Svenja Leibniz Univ Hannover Inst Mech Syst Hannover Germany Leibniz Univ Hannover Inst Informat Proc Hannover Germany

ISBN: (纸本)9783031164316;9783031164309

computer aided diagnosis (CAD) has gained an increased amount of attention in the general research community over the last years as an example of a typical limited data application - with experiments on labeled 100k-200k datasets. Although these datasets are still small compared to natural image datasets like imageNet1k, imageNet21k and JFT, they are large for annotated medical datasets, where 1k-10k labeled samples are much more common. there is no baseline on which methods to build on in the low data regime. In this work we bridge this gap by providing an extensive study on medical image classification with limited annotations (5k). We present a study of modern architectures applied to a fixed low data regime of 5000 images on the CheXpert dataset. Conclusively we find that models pretrained on imageNet21k achieve a higher AUC and larger models require less training steps. All models are quite well calibrated even though we only fine-tuned on 5000 training samples. All 'modern' architectures have higher AUC than ResNet50. Regularization of Big Transfer Models with MixUp or Mean Teacher improves calibration, MixUp also improves accuracy. Vision Transformer achieve comparable or on par results to Big Transfer Models.

关键词： medical image classification Limited data Transfer learning

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Fourier Test-Time Adaptation with Multi-level Consistency for Robust Classification 1

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

作者： Huang, Yuhao Yang, Xin Huang, Xiaoqiong Zhou, Xinrui Chi, Haozhe Dou, Haoran Hu, Xindi Wang, Jian Deng, Xuedong Ni, Dong Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Zhejiang Univ ZJU UIUC Inst Hangzhou Peoples R China Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed Leeds W Yorkshire England Shenzhen RayShape Med Technol Co Ltd Shenzhen Peoples R China Nanjing Med Univ Sch Biomed Engn & Informat Nanjing Peoples R China Nanjing Med Univ Affiliated Suzhou Hosp Suzhou Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Deep classifiers may encounter significant performance degradation when processing unseen testing data from varying centers, vendors, and protocols. Ensuring the robustness of deep models against these domain shifts is crucial for their widespread clinical application. In this study, we propose a novel approach called Fourier Test-time Adaptation (FTTA), which employs a dual-adaptation design to integrate input and model tuning, thereby jointly improving the model robustness. the main idea of FTTA is to build a reliable multi-level consistency measurement of paired inputs for achieving self-correction of prediction. Our contribution is two-fold. First, we encourage consistency in global features and local attention maps between the two transformed images of the same input. Here, the transformation refers to Fourier-based input adaptation, which can transfer one unseen image into source style to reduce the domain gap. Furthermore, we leverage style-interpolated images to enhance the global and local features with learnable parameters, which can smooth the consistency measurement and accelerate convergence. Second, we introduce a regularization technique that utilizes style interpolation consistency in the frequency space to encourage self-consistency in the logit space of the model output. this regularization provides strong self-supervised signals for robustness enhancement. FTTA was extensively validated on three large classification datasets with different modalities and organs. Experimental results show that FTTA is general and outperforms other strong state-of-the-art methods.

关键词： Classifier robustness Testing-time adaptation Consistency

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Temporal Uncertainty Localization to Enable Human-in-the-Loop Analysis of Dynamic Contrast-Enhanced Cardiac MRI Datasets 1

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

作者： Yalcinkaya, Dilek M. Youssef, Khalid Heydari, Bobak Simonetti, Orlando Dharmakumar, Rohan Raman, Subha Sharif, Behzad Indiana Univ Sch Med IUSM Lab Translat Imaging Microcirculat Indianapolis IN 46202 USA Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA IUSM IU Hlth Cardiovasc Inst Krannert Cardiovasc Res Ctr Indianapolis IN USA Univ Calgary Stephenson Cardiac Imaging Ctr Calgary AB Canada Ohio State Univ Davis Heart & Lung Res Inst Div Cardiovasc Med Dept Internal Med Columbus OH USA Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47907 USA

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. this approach resulted in a significant increase in the Dice score (p< 0.001) and a notable decrease in the number of images with failed segmentation (16.2% to 11.3%) whereas the alternative approach of randomly selecting the same number of segmentations for human referral did not achieve any significant improvement. Our results suggest that the proposed dQC framework has the potential to accurately identify poor-quality segmentations and may enable efficient DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical interpretation and reporting of dynamic CMRI datasets.

关键词： Cardiovascular MRI Dynamic MRI image Segmentation Quality control Uncertainty Quantification Human-in-the-loop A.I

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SMESwin Unet: Merging CNN and Transformer for medical image Segmentation 25th

SMESwin Unet: Merging CNN and Transformer for Medical Image ...

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

作者： Wang, Ziheng Min, Xiongkuo Shi, Fangyu Jin, Ruinian Nawrin, Saida S. Yu, Ichen Nagatomi, Ryoichi Tohoku Univ Grad Sch Biomed Engn Div Biomed Engn Hlth & Welf Sendai Japan Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Tohoku Univ Grad Sch Med Dept Med & Sci Sports & Exercise Sendai Japan

ISBN: (纸本)9783031164439;9783031164422

Vision transformer is the new favorite paradigm in medical image segmentation since last year, which surpassed the traditional CNN counterparts in quantitative metrics. the significant advantage of ViTs is to utilize the attention layers to model global relations between tokens. However, the increased representation capacity of ViTs comes with corresponding shortcomings: short of CNN's inductive biases (locality), translation invariance, and hierarchical structure of visual information. Consequently, well-trained ViTs require more data than CNNs. As high quality data in medical imaging area is always limited, we propose SMESwin UNet. Firstly, based on Channel-wise Cross fusion Transformer (CCT) we fuse multi-scale semantic features and attention maps by designing a compound structure with CNN and ViTs (named MCCT). Secondly, we introduce superpixel by dividing the pixel-level feature into district-level to avoid the interference of meaningless parts of the image. Finally, we used External Attention to consider the correlations among all data samples, which may further reduce the limitation of small datasets. According to our experiments, the proposed superpixel and MCCT-based Swin Unet (SMESwin Unet) achieves better performance than CNNs and other Transformer-based architectures on three medical image segmentation datasets (nucleus, cells, and glands).

关键词： Superpixels

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Scribble2D5: Weakly-Supervised Volumetric image Segmentation via Scribble Annotations 25th

Scribble2D5: Weakly-Supervised Volumetric Image Segmentation...

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

作者： Chen, Qiuhui Hong, Yi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9783031164521;9783031164514

image segmentation using weak annotations like scribbles has gained great attention, since such annotations are easier to obtain compared to time-consuming and labor-intensive labeling at the pixel/voxel level. However, scribbles lack structure information of the region of interest (ROI), thus existing scribble-based methods suffer from poor boundary localization. Moreover, current methods are mostly designed for 2D image segmentation, which do not fully leverage volumetric information. In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and improves boundary predictions. To achieve this, we augment a 2.5D attention UNet with a proposed label propagation module to extend semantic information from scribbles and a combination of static and active boundary prediction to learn ROI's boundaries and regularize its shape. Extensive experiments on three public datasets demonstrate Scribble2D5 significantly outperforms existing scribble-based methods and approaches the performance of fully-supervised ones. Our code is available at https://***/Qybc/Scribble2D5.

关键词： Weakly-supervised learning Scribble annotation Volumetric image segmentation

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Deep Learning for MRI-Based Brain Tumour Identification and Classification 1

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8th international conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Jareena Begum, D. Chokkalingam, S.P. Sundaravadivazhagan, B. Department of Computer Science and Engineering Amrita School of Computing Chennai Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Chennai Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology University of Technology and Applied Sciences University of Technology and Applied Sciences Al Mussana Oman

ISBN: (数字)9789819732456

ISBN: (纸本)9789819732449

Radiology tumour spotting is complicated and requires medical knowledge. thus, a lack of doctors should not delay cancer detection programmes. Biomedical image processing software helps find brain tumours in MRI data. this study segmented and detected brain tumours using MRI sequence images. this process is complicated by the similarity of normal tissues and the wide range of tumour tissues in different patients. Brain tumour detection is the main goal. Brain tumour diagnosis requires precise tumour size and position. this paper presents a deep learning-based brain tumour MRI segmentation and classification method. We preprocessed the photos with the Gaussian blur filter and image enhancement tool. Binary thresholding segments. Morphological methods reveal traits. CNN will assess brain MRI regularity. this study uses the Kaggle Dataset. We train with 255 brain MRIs, 155 with tumours and 98 normal, and achieve 97% accuracy. We test the model using Kaggle brain MR datasets. Brain tumour detection, segmentation, and categorization;medical image processing. this study proposed an easy-to-implement way for identifying and classifying bone tumours. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tumors

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Semi-supervised medical image Segmentation Using Cross-Model Pseudo-Supervision with Shape Awareness and Local Context Constraints 25th

Semi-supervised Medical Image Segmentation Using Cross-Model...

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

作者： Liu, Jinhua Desrosiers, Christian Zhou, Yuanfeng Shandong Univ Sch Software Jinan Peoples R China Ecole Technol Super Software & IT Engn Dept Montreal PQ Canada

ISBN: (纸本)9783031164521;9783031164514

In semi-supervised medical image segmentation, the limited amount of labeled data available for training is often insufficient to learn the variability and complexity of target regions. To overcome these challenges, we propose a novel framework based on cross-model pseudo-supervision that generates anatomically plausible predictions using shape awareness and local context constraints. Our framework consists of two parallel networks, a shape-aware network and a shape-agnostic network, which provide pseudo-labels to each other for using unlabeled data effectively. the shape-aware network implicitly captures information on the shape of target regions by adding the prediction of the other network as input. On the other hand, the shape-agnostic network leverages Monte-Carlo dropout uncertainty estimation to generate reliable pseudo-labels to the other network. the proposed framework also comprises a new loss function that enables the network to learn the local context of the segmentation, thus improving the overall segmentation accuracy. Experiments on two publicly-available datasets show that our method outperforms state-of-the-art approaches for semi-supervised segmentation and better preserves anatomical morphology compared to these approaches. Code is available at https://***/igip-liu/SLC-Net.

关键词： Semi-supervised segmentation Shape awareness Cross-model pseudo-supervision Local context loss

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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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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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