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Triple-View Feature Learning for medical image Segmentation 1

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1st international Workshop on Resource-Efficient medical image Analysis (REMIA) / 25th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Wang, Ziyang Voiculescu, Irina Univ Oxford Dept Comp Sci Oxford England

ISBN: (数字)9783031168765

ISBN: (纸本)9783031168765;9783031168758

Deep learning models, e.g. supervised Encoder-Decoder style networks, exhibit promising performance in medical image segmentation, but come with a high labelling cost. We propose TriSegNet, a semi-supervised semantic segmentation framework. It uses triple-view feature learning on a limited amount of labelled data and a large amount of unlabeled data. the triple-view architecture consists of three pixel-level classifiers and a low-level shared-weight learning module. the model is first initialized with labelled data. Label processing, including data perturbation, confidence label voting and unconfident label detection for annotation, enables the model to train on labelled and unlabeled data simultaneously. the confidence of each model gets improved through the other two views of the feature learning. this process is repeated until each model reaches the same confidence level as its counterparts. this strategy enables triple-view learning of generic medical image datasets. Bespoke overlap-based and boundary-based loss functions are tailored to the different stages of the training. the segmentation results are evaluated on four publicly available benchmark datasets including Ultrasound, CT, MRI, and Histology images. Repeated experiments demonstrate the effectiveness of the proposed network compared against other semi-supervised algorithms, across a large set of evaluation measures.

关键词： Deep learning

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DOMINO: Domain-Aware Model Calibration in medical image Segmentation 25th

DOMINO: Domain-Aware Model Calibration in Medical Image Segm...

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

作者： Stolte, Skylar E. Volle, Kyle Indahlastari, Aprinda Albizu, Alejandro Woods, Adam J. Brink, Kevin Hale, Matthew Fang, Ruogu Univ Florida UF J Crayton Pruitt Family Dept Biomed Engn Herbert Wertheim Coll Engn Gainesville FL 32611 USA UF Herbert Wertheim Coll Engn Dept Mech & Aerosp Engn Gainesville FL USA UF McKnight Brain Inst Ctr Cognit Aging & Memory Gainesville FL 32611 USA UF Coll Publ Hlth & Hlth Pro Dept Clin & Hlth Psychol Gainesville FL USA UF Coll Med Dept Neurosci Gainesville FL USA US Air Force Res Lab Eglin AFB FL USA UF Dept Elect & Comp Engn Herbert Wertheim Coll Engn Gainesville FL 32611 USA

ISBN: (纸本)9783031164439;9783031164422

Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern deep neural networks are poorly calibrated, compromising trustworthiness and reliability. medical image segmentation particularly suffers from this due to the natural uncertainty of tissue boundaries. this is exasperated by their loss functions, which favor overconfidence in the majority classes. We address these challenges with DOMINO, a domain-aware model calibration method that leverages the semantic confusability and hierarchical similarity between class labels. Our experiments demonstrate that our DOMINO-calibrated deep neural networks outperform non-calibrated models and state-of-the-art morphometric methods in head image segmentation. Our results show that our method can consistently achieve better calibration, higher accuracy, and faster inference times than these methods, especially on rarer classes. this performance is attributed to our domain-aware regularization to inform semantic model calibration. these findings show the importance of semantic ties between class labels in building confidence in deep learning models. the framework has the potential to improve the trustworthiness and reliability of generic medical image segmentation models.

关键词： image segmentation Machine learning uncertainty Model calibration Model generalizability Whole head MRI

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Benchmark of Deep Encoder-Decoder Architectures for Head and Neck Tumor Segmentation in Magnetic Resonance images: Contribution to the HNTSMRG Challenge 1st

Benchmark of Deep Encoder-Decoder Architectures for Head and...

引用

1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Wodzinski, Marek Department of Measurement and Electronics AGH University of Krakow Krakow Poland Sierre Switzerland

ISBN: (纸本)9783031832734

Radiation therapy is one of the most frequently applied cancer treatments worldwide, especially in the context of head and neck cancer. Today, MRI-guided radiation therapy planning is becoming increasingly popular due to good soft tissue contrast, lack of radiation dose delivered to the patient, and the capability of performing functional imaging. However, MRI-guided radiation therapy requires segmenting of the cancer both before and during radiation therapy. So far, the segmentation was often performed manually by experienced radiologists, however, recent advances in deep learning-based segmentation suggest that it may be possible to perform the segmentation automatically. Nevertheless, the task is arguably more difficult when using MRI compared to e.g. PET-CT because even manual segmentation of head and neck cancer in MRI volumes is challenging and time-consuming. the importance of the problem motivated the researchers to organize the HNTSMRG challenge with the aim of developing the most accurate segmentation methods, both before and during MRI-guided radiation therapy. In this work, we benchmark several different state-of-the-art segmentation architectures to verify whether the recent advances in deep encoder-decoder architectures are impactful for low data regimes and low-contrast tasks like segmenting head and neck cancer in magnetic resonance images. We show that for such cases the traditional residual UNet-based method outperforms (DSC = 0.775/0.701) recent advances such as UNETR (DSC = 0.617/0.657), SwinUNETR (DSC = 0.757/0.700), or SegMamba (DSC = 0.708/0.683). the proposed method (lWM team) achieved a mean aggregated Dice score on the closed test set at the level of 0.771 and 0.707 for the pre- and mid-therapy segmentation tasks, scoring 14th and 6th place, respectively. the results suggest that proper data preparation, objective function, and preprocessing are more influential for the segmentation of head and neck cancer than deep network architecture. ©

关键词： image segmentation

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AutoLaparo: A New Dataset of Integrated Multi-tasks for image-guided Surgical Automation in Laparoscopic Hysterectomy 25th

AutoLaparo: A New Dataset of Integrated Multi-tasks for Imag...

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

作者： Wang, Ziyi Lu, Bo Long, Yonghao Zhong, Fangxun Cheung, Tak-Hong Dou, Qi Liu, Yunhui Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Chinese Univ Hong Kong T Stone Robot Inst Hong Kong Peoples R China Soochow Univ Sch Mech & Elect Engn Robot & Microsyst Ctr Suzhou Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Univ Hong Kong Prince Wales Hosp Dept Obstet & Gynaecol Hong Kong Peoples R China

ISBN: (纸本)9783031164491;9783031164484

computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. the video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during the procedure, learning based technique is a promising way, which enables advanced image analysis and scene understanding in recent years. However, learning such models highly relies on large-scale, high-quality, and multi-task labelled data. this is currently a bottleneck for the topic, as available public dataset is still extremely limited in the field of CAI. In this paper, we present and release the first integrated dataset (named AutoLaparo) with multiple image-based perception tasks to facilitate learning-based automation in hysterectomy surgery. Our AutoLaparo dataset is developed based on full-length videos of entire hysterectomy procedures. Specifically, three different yet highly correlated tasks are formulated in the dataset, including surgical workflow recognition, laparoscope motion prediction, and instrument and key anatomy segmentation. In addition, we provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset. the dataset is available at https://***.

关键词： Surgical video dataset image-guided robotic surgical automation Laparoscopic hysterectomy

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Deep Neural Network Optimization for Skin Disease Classification Forecast Analysis 8

Deep Neural Network Optimization for Skin Disease Classifica...

引用

8th international conference on Advanced computing and Communication Systems, ICACCS 2022

作者： Kalaivani, A. Karpagavalli, S. Nallamuthu Gounder Mahalingam College Department of Computer Technology Tamilnadu Coimbatore India Psgr Krishnammal College for Women Department of Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9781665408165

Skin lesions are a prevalent condition that causes misery, many of which can be severe, for millions of individuals worldwide. Consequently, Deep learning seems to be an increasingly popular approach in recent years, and it may be a strong tool in difficult, earlier domains, specifically in health science, which is now dealing with a number of medical resources. In this paper, presented an interactive dermoscopy images diagnosis framework based on an gathering of intelligent deep learning model system for image classification to make advances their person accuracies within the prepare of classifying dermoscopy pictures into several classes such as melanoma, keratosis and nevus when we have not sufficient annotated images to train them on. We integrate the classification layer results for two distinct deep neural network designs to obtain excellent classification accuracy. More precisely, we combining robust convolutional neural networks (CNNs) into a unified structure, with the final classification relying on the weighted outcome of the respective CNNs by predictive ensemble methods and fine-tuning classifiers utilizing ISIC2019 images. Furthermore, the outliers and the substantial class imbalance are handled in order to improve the categorization of the disease. the experimental reveal that the framework produced result that are comparable to other models of conventional art. A substantial improvement in accuracy of 96.2 percentage indicated the efficiency of the proposed Predictive Ensemble Deep Convolutional Neural Networks Classifier (PE-DCNN Classifier) model and this study effectively built a system with all the important features. © 2022 IEEE.

关键词： image classification

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Weakly-Supervised High-Fidelity Ultrasound Video Synthesis with Feature Decoupling 1

引用

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

作者： Liang, Jiamin Yang, Xin Huang, Yuhao Liu, Kai Zhou, Xinrui Hu, Xindi Lin, Zehui Luo, Huanjia Zhang, Yuanji Xiong, Yi 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 Shenzhen RayShape Med Technol Co Ltd Shenzhen Peoples R China Huizhou Cent Peoples Hosp Huizhou Guangdong Peoples R China Luohu Peoples Hosp Dept Ultrasound Shenzhen Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Ultrasound (US) is widely used for its advantages of real-time imaging, radiation-free and portability. In clinical practice, analysis and diagnosis often rely on US sequences rather than a single image to obtain dynamic anatomical information. this is challenging for novices to learn because practicing with adequate videos from patients is clinically unpractical. In this paper, we propose a novel framework to synthesize high-fidelity US videos. Specifically, the synthesis videos are generated by animating source content images based on the motion of given driving videos. Our highlights are three-fold. First, leveraging the advantages of self- and fully-supervised learning, our proposed system is trained in weakly-supervised manner for keypoint detection. these keypoints then provide vital information for handling complex high dynamic motions in US videos. Second, we decouple content and texture learning using the dual decoders to effectively reduce the model learning difficulty. Last, we adopt the adversarial training strategy with GAN losses for further improving the sharpness of the generated videos, narrowing the gap between real and synthesis videos. We validate our method on a large in-house pelvic dataset with high dynamic motion. Extensive evaluation metrics and user study prove the effectiveness of our proposed method.

关键词： Diagnosis

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Moving from 2D to 3D: Volumetric medical image Classification for Rectal Cancer Staging 1

引用

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

作者： Lee, Joohyung Oh, Jieun Shin, Inkyu Kim, You-Sung Sohn, Dae Kyung Kim, Tae-Sung Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea Natl Canc Ctr Healthcare A1 Team Goyang South Korea Natl Canc Ctr Dept Radiol Goyang South Korea Natl Canc Ctr Ctr Colorectal Canc Goyang South Korea

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Volumetric images from Magnetic Resonance Imaging (MRI) provide invaluable information in preoperative staging of rectal cancer. Above all, accurate preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment, as chemo-radiotherapy is usually recommended for patients with T3 (or greater) stage cancer. In this study, we present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes. Specifically, we propose 1) a custom ResNet-based volume encoder that models the inter-slice relationship with late fusion (i.e., 3D convolution at the last layer), 2) a bilinear computation that aggregates the resulting features from the encoder to create a volume-wise feature, and 3) a joint minimization of triplet loss and focal loss. With MR volumes of pathologically confirmed T2/T3 rectal cancer, we perform extensive experiments to compare various designs within the framework of residual learning. As a result, our network achieves an AUC of 0.831, which is higher than the reported accuracy of the professional radiologist groups. We believe this method can be extended to other volume analysis tasks.

关键词： Rectal cancer staging medical volume classification Convolutional neural network Anisotropy

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Domain-Agnostic Segmentation of thalamic Nuclei from Joint Structural and Diffusion MRI 26th

Domain-Agnostic Segmentation of Thalamic Nuclei from Joint S...

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

作者： Tregidgo, Henry F. J. Soskic, Sonja Olchanyi, Mark D. Althonayan, Juri Billot, Benjamin Maffei, Chiara Golland, Polina Yendiki, Anastasia Alexander, Daniel C. Bocchetta, Martina Rohrer, Jonathan D. Iglesias, Juan Eugenio UCL Ctr Med Image Comp London England MIT Neurostatist Res Lab Cambridge MA USA MGH Neurotechnol & Neurorecovery Boston MA USA Harvard Med Sch Boston MA 02115 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA MGH Martinos Ctr Biomed Imaging Boston MA USA Brunel Univ Ctr Cognit & Clin Neurosci London England UCL Dementia Res Ctr London England

ISBN: (纸本)9783031439926;9783031439933

the human thalamus is a subcortical brain structure that comprises dozens of nuclei with different function and connectivity, which are affected differently by disease. For this reason, there is growing interest in studying the thalamic nuclei in vivo with MRI. Tools are available to segment the thalamus from 1mm T1 scans, but the image contrast is too faint to produce reliable segmentations. Some tools have attempted to refine these boundaries using diffusion MRI information, but do not generalise well across diffusion MRI acquisitions. Here we present the first CNN that can segment thalamic nuclei from T1 and diffusion data of any resolution without retraining or fine tuning. Our method builds on our histological atlas of the thalamic nuclei and silver standard segmentations on high-quality diffusion data obtained with our recent Bayesian adaptive segmentation tool. We combine these with an approximate degradation model for fast domain randomisation during training. Our CNN produces a segmentation at 0.7mm isotropic resolution, irrespective of the resolution of the input. Moreover, it uses a parsimonious model of the diffusion signal (fractional anisotropy and principal eigenvector) that is compatible with virtually any set of directions and b-values, including huge amounts of legacy data. We show results of our proposed method on three heterogeneous datasets acquired on dozens of different scanners. the method is publicly available at ***/fswiki/thalamicNucleiDTI.

关键词： Magnetic resonance imaging

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Whole Slide Cervical Cancer Screening Using Graph Attention Network and Supervised Contrastive Learning 25th

Whole Slide Cervical Cancer Screening Using Graph Attention ...

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

作者： Zhang, Xin Cao, Maosong Wang, Sheng Sun, Jiayin Fan, Xiangshan Wang, Qian Zhang, Lichi Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China Shanghai Tech Univ Sch Biomed Engn Shanghai Peoples R China Nanjing Univ Affiliated Drum Tower Hosp Dept Pathol Med Sch Nanjing Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Cervical cancer is one of the primary factors that endanger women's health, and thin-prep cytologic test (TCT) has been widely applied for early screening. Automatic whole slide image (WSI) classification is highly demanded, as it can significantly reduce the workload of pathologists. Current methods are mainly based on suspicious lesion patch extraction and classification, which ignore the intrinsic relationships between suspicious patches and neglect the other patches apart from the suspicious patches, and therefore limit their robustness and generalizability. Here we propose a novel method to solve the problem, which is based on graph attention network (GAT) and supervised contrastive learning. First, for each WSI, we extract and rank a large number of representative patches based on suspicious cell detection. then, we select the top-K and bottom-K suspicious patches to construct two graphs seperately. Next, we introduce GAT to aggregate the features from each node, and use supervised contrastive learning to obtain valuable representations of graphs. Specifically, we design a novel contrastive loss so that the latent distances between two graphs are enlarged for positive WSIs and reduced for negative WSIs. Experimental results show that the proposed GAT method outperforms conventional methods, and also demonstrate the effectiveness of supervised contrastive learning.

关键词： Cervical cancer Whole slide image classification Graph attention network Supervised contrastive learning

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Transformer Based Feature Fusion for Left Ventricle Segmentation in 4D Flow MRI 25th

Transformer Based Feature Fusion for Left Ventricle Segmenta...

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

作者： Sun, Xiaowu Cheng, Li-Hsin Plein, Sven Garg, Pankaj van der Geest, Rob J. Leiden Univ Div Image Proc Dept Radiol Med Ctr Leiden Netherlands Univ Leeds Leeds Inst Cardiovasc & Metab Med Leeds England Univ East Anglia Norwich Med Sch Norwich England

ISBN: (纸本)9783031164439;9783031164422

Four-dimensional flow magnetic resonance imaging (4D Flow MRI) enables visualization of intra-cardiac blood flow and quantification of cardiac function using time-resolved three directional velocity data. Segmentation of cardiac 4D flow data is a big challenge due to the extremely poor contrast between the blood pool and myocardium. the magnitude and velocity images from a 4D flow acquisition provide complementary information, but how to extract and fuse these features efficiently is unknown. Automated cardiac segmentation methods from 4D flow MRI have not been fully investigated yet. In this paper, we take the velocity and magnitude image as the inputs of two branches separately, then propose a Transformer based cross- and self-fusion layer to explore the inter-relationship from two modalities and model the intra-relationship in the same modality. A large in-house dataset of 104 subjects (91,182 2D images) was used to train and evaluate our model using several metrics including the Dice, Average Surface Distance (ASD), end-diastolic volume (EDV), end-systolic volume (ESV), Left Ventricle Ejection Fraction (LVEF) and Kinetic Energy (KE). Our method achieved a mean Dice of 86.52%, and ASD of 2.51 mm Evaluation on the clinical parameters demonstrated competitive results, yielding a Pearson correlation coefficient of 83.26%, 97.4%, 96.97% and 98.92% for LVEF, EDV, ESV and KE respectively.

关键词： LV segmentation 4D Flow MRI Feature fusion Transformer

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