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A Coarse-to-Fine Framework for Mid-Radiotherapy Head and Neck Cancer MRI Segmentation 1st

A Coarse-to-Fine Framework for Mid-Radiotherapy Head and Nec...

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： Ni, Jing Yao, Qiulei Liu, Yanfei Qi, Haikun ShanghaiTech University Shanghai China Shenzhen United Imaging Research Institute of Innovative Medical Equipment Shenzhen China

ISBN: (纸本)9783031832734

Radiotherapy is the preferred treatment modality for head and neck cancer (HNC). During the treatment, adaptive radiation therapy (ART) technology is commonly employed to account for changes in target volume and alter- ations in patient anatomy. this adaptability ensures that treatment remains precise and effective despite these physiological variations. Magnetic resonance imaging (MRI) provides higher-resolution soft tissue images, making it valuable in target delineation of HNC treatment. the delineation in ART should adhere to the same principles as those used in the initial delineation. Consequently, the contouring performed on MR images during ART should reference the earlier delineations for consistency and accuracy. To address this, we proposed a coarse-to-fine cascade framework based on 3D U-Net to segment mid-radiotherapy HNC from T2- weighted MRI. the model consists of two interconnected components: a coarse segmentation network and a fine segmentation network, both sharing the same architecture. In the coarse segmentation phase, different forms of prior information were used as input, including dilated pre-radiotherapy masks. In the fine segmentation phase, a resampling operation based on a bounding box focuses on the region of interest, refining the prediction with the mid-radiotherapy image to achieve the final segmentation. In our experiment, the final results were achieved with an aggregated Dice Similarity Coefficient (DSC) of 0.562, indicating that the prior information plays a crucial role in enhancing segmentation accuracy. (Team name: TNL_skd) © the Author(s) 2025.

关键词： image segmentation

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Multi-modal Retinal image Registration Using a Keypoint-Based Vessel Structure Aligning Network 25th

Multi-modal Retinal Image Registration Using a Keypoint-Base...

引用

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

作者： Sindel, Aline Hohberger, Bettina Maier, Andreas Christlein, Vincent FAU Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany Univ Klinikum Erlangen Dept Ophthalmol Erlangen Germany

ISBN: (纸本)9783031164460;9783031164453

In ophthalmological imaging, multiple imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography, are often involved to make a diagnosis of retinal disease. Multi-modal retinal registration techniques can assist ophthalmologists by providing a pixel-based comparison of aligned vessel structures in images from different modalities or acquisition times. To this end, we propose an end-to-end trainable deep learning method for multi-modal retinal image registration. Our method extracts convolutional features from the vessel structure for keypoint detection and description and uses a graph neural network for feature matching. the keypoint detection and description network and graph neural network are jointly trained in a self-supervised manner using synthetic multi-modal image pairs and are guided by synthetically sampled ground truth homographies. Our method demonstrates higher registration accuracy as competing methods for our synthetic retinal dataset and generalizes well for our real macula dataset and a public fundus dataset.

关键词： Multi-modal retinal image registration Convolutional neural networks Graph neural networks

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Brain Tumor Detection using Deep Learning 5

Brain Tumor Detection using Deep Learning

引用

5th IEEE international conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Rai, Vipin Dwivedi, Harshit Tiwari, Shivam Gupta, Pankaj Kumar Galgotias University School Of Computing Science And Engineering Plot No-2 Sector 17-A Yamuna Expressway Greater Noida India

ISBN: (纸本)9798350330861

At first, people were unaware of brain tumors, it was believed that there was no cure for the disease, and the main control of the humanoid system was carried out by the human brain. Brain tumors form when brain cells grow and divide abnormally, and brain cancers form when brain tumors continue to grow. computer purpose is essential in the field of medical because it removes the need for human judgment to produce correct results. the safest MRI techniques are likes of X-ray, CT-Scan and many more useful techniques. Such techniques can even identify small objects. Brain cancer using brain MRI. Abilateral (BF) filter was used in the pre-processing of this study to remove any noise present. For clinical anaysis there is a method termed as medical imaging is used which basically provides visual representation of the inshore of body and also to show its functioning. medical imaging is becoming increasingly important as the need for automatic, quick and efficient diagnosis increases, which can provide better image understanding than the human eye. Seeing different trends among young age group it can be said the brain tumor might be the major source of deaths caused by cancer among age group of 20-39. Brain tumors are painful and, if not treated properly, can eventually lead to various diseases. © 2023 IEEE.

关键词： Diseases

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intervention & Interaction Federated Abnormality Detection with Noisy Clients 25th

Intervention & Interaction Federated Abnormality Detection w...

引用

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

作者： Liu, Xinyu Li, Wuyang Yuan, Yixuan City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031164521;9783031164514

Federated learning (FL), which trains a shared global model by collaboration between distributed clients (e.g. medical institutions) and preserves the privacy of local data, has been widely deployed in the medical field to benefit abnormality diagnosis. However, it is inevitable that local data contains noise across clients, resulting in notably performance deterioration in the global model. To this end, a practical yet challenging FL problem is studied in this paper, namely Federated abnormality detection with noisy clients (FADN). We represent the first effort to reason the FADN task as a structural causal model, and identify the main issue that leads to the performance deterioration, namely recognition bias. To tackle the problem, an intervention & Interaction FL framework (FedInI) is proposed, comprising two key strategies: (1) intervention: considering the data distribution heterogeneity caused by different noisy levels within each client, we use the global model to intervene the training of local models, by shuffling and mixing features extracted from different models and suppress the noise gradually;(2) Interaction: we devise an adaptive sample-wise weighting strategy that jointly considers the local training statuses and global noisy levels with a shared interactive layer. Extensive experiments on class-conditional noise and instance-dependant noise settings are conducted, FedInI outperforms state-of-the-arts by a remarkable margin. Code is available at github. corn/CityU-AIM-Group/FedInl.

关键词： Federated learning Learning with noisy labels Abnormality detection Causal intervention

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Sensor Geometry Generalization to Untrained Conditions in Quantitative Ultrasound Imaging 25th

Sensor Geometry Generalization to Untrained Conditions in Qu...

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

作者： Oh, SeokHwan Kim, Myeong-Gee Kim, Youngmin Jung, Guil Kwon, Hyuksool Bae, Hyeon-Min Korea Adv Inst Sci & Technol Elect Engn Dept Daejeon 34141 South Korea Seoul Natl Univ Dept Emergency Med Bundang Hosp Seongnam 13620 South Korea

ISBN: (纸本)9783031164460;9783031164453

Recent improvements in deep learning have brought great progress in ultrasonic lesion quantification. However, the learning-based scheme performs properly only when a certain level of similarity between train and test condition is ensured. However, real-world test condition expects diverse untrained probe geometry from various manufacturers, which undermines the credibility of learning-based ultrasonic approaches. In this paper, we present a meta-learned deformable sensor generalization network that generates consistent attenuation coefficient (AC) image regardless of the probe condition. the proposed method was assessed through numerical simulation and in-vivo breast patient measurements. the numerical simulation shows that the proposed network outperforms existing state-of-the-art domain generalization methods for the AC reconstruction under unseen probe conditions. In in-vivo studies, the proposed network provides consistent AC images irrespective of various probe conditions and demonstrates great clinical potential in differential breast cancer diagnosis.

关键词： medical ultrasound Domain generalization image reconstruction Quantitative ultrasound imaging Meta-learning

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Deep Regression with Spatial-Frequency Feature Coupling and image Synthesis for Robot-assisted Endomicroscopy 25th

Deep Regression with Spatial-Frequency Feature Coupling and ...

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

作者： Xu, Chi Roddan, Alfie Davids, Joseph Weld, Alistair Xu, Haozheng Giannarou, Stamatia Imperial Coll London Dept Surg & Canc Hamlyn Ctr Robot Surg London SW7 2AZ England

ISBN: (纸本)9783031164491;9783031164484

Probe-based confocal laser endomicroscopy (pCLE) allows in-situ visualisation of cellular morphology for intraoperative tissue characterization. Robotic manipulation of the pCLE probe can maintain the probe-tissue contact within micrometre working range to achieve the precision and stability required to capture good quality microscopic information. In this paper, we propose the first approach to automatically regress the distance between a pCLE probe and the tissue surface during robotic tissue scanning. the Spatial-Frequency Feature Coupling network (SFFC-Net) was designed to regress probe-tissue distance by extracting an enhanced data representation based on the fusion of spatial and frequency domain features. image-level supervision is used in a novel fashion in regression to enable the network to effectively learn the relationship between the sharpness of the pCLE image and its distance from the tissue surface. Consequently, a novel Feedback Training (FT) module has been designed to synthesise unseen images to incorporate feedback into the training process. the first pCLE regression dataset (PRD) was generated which includes ex-vivo images with corresponding probe-tissue distance. Our performance evaluation verifies that the proposed network outperforms other state-of-the-art (SOTA) regression networks.

关键词： Regression Fourier convolution Endomicroscopy

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Transformer Based Multi-view Network for Mammographic image Classification 1

引用

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

作者： Sun, Zizhao Jiang, Huiqin Ma, Ling Yu, Zhan Xu, Hongwei Zhengzhou Univ Zhengzhou Peoples R China Hosp Zhengzhou Univ Affiliated 1 Zhengzhou Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Most of the existing multi-view mammographic image analysis methods adopt a simple fusion strategy: features concatenation, which is widely used in many features fusion methods. However, concatenation based methods can't extract cross view information very effectively because different views are likely to be unaligned. Recently, many researchers have attempted to introduce attention mechanism related methods into the field of multi-view mammography analysis. But these attention mechanism based methods still partly rely on convolution, so they can't take full advantages of attention mechanism. To take full advantage of multi-view information, we propose a novel pure transformer based multi-view network to solve the question of mammographic image classification. In our primary network, we use a transformer based backbone network to extract image features, a "cross view attention block" structure to fuse multi-view information, and a "classification token" to gather all useful information to make the final prediction. Besides, we compare the performance when fusing multi-view information at different stages of the backbone network using a novel designed "(shifted) window based cross view attention block" structure and compare the results when fusing different views' information. the results on DDSM dataset show that our networks can effectively use multi-view information to make judgments and outperform the concatenation and convolution based methods.

关键词： Multi-view Mammographic image classification Transformer Cross view attention

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Test-Time Adaptation with Calibration of medical image Classification Nets for Label Distribution Shift 1

引用

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

作者： Ma, Wenao Chen, Cheng Zheng, Shuang Qin, Jing Zhang, Huimao Dou, Qi Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China First Hosp Jilin Univ Dept Radiol Changchun Peoples R China Jilin Prov Key Lab Med Imaging Big Data Changchun Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Kowloon Hong Kong Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classification nets usually degrades. Such a label distribution shift problem is common in medical diagnosis since the prevalence of disease vary over location and time. In this paper, we propose the first method to tackle label shift for medical image classification, which effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution. Our approach innovates distribution calibration to learn multiple representative classifiers, which are capable of handling different one-dominating-class distributions. When given a test image, the diverse classifiers are dynamically aggregated via the consistency-driven test-time adaptation, to deal with the unknown test label distribution. We validate our method on two important medical image classification tasks including liver fibrosis staging and COVID-19 severity prediction. Our experiments clearly show the decreased model performance under label shift. With our method, model performance significantly improves on all the test datasets with different label shifts for both medical image diagnosis tasks.

关键词： Test-time adaptation Label distribution shift medical image classification

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Dynamic Bank Learning for Semi-supervised Federated image Diagnosis with Class Imbalance 1

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

作者： Jiang, Meirui Yang, Hongzheng Li, Xiaoxiao Liu, Quande Heng, Pheng-Ann Dou, Qi Chinese Univ Hong Kong Dept Comp Sci & Engn Sha Tin Hong Kong Peoples R China Beihang Univ Dept Artificial Intelligence Beijing Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use. In this paper, we study a practical yet challenging problem of class imbalanced semi-supervised FL (imFed-Semi), which allows all clients to have only unlabeled data while the server just has a small amount of labeled data. this imFed-Semi problem is addressed by a novel dynamic bank learning scheme, which improves client training by exploiting class proportion information. this scheme consists of two parts, i.e., the dynamic bank construction to distill various class proportions for each local client, and the sub-bank classification to impose the local model to learn different class proportions. We evaluate our approach on two public real-world medical datasets, including the intracranial hemorrhage diagnosis with 25,000 CT slices and skin lesion diagnosis with 10,015 dermoscopy images. the effectiveness of our method has been validated with significant performance improvements (7.61% and 4.69%) compared with the second-best on the accuracy, as well as comprehensive analytical studies.

关键词： Semi-supervised FL Class imbalance image diagnosis

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Internet of medical things: A CT image Denoising in Tetrolet Domain 5

Internet of Medical Things: A CT Image Denoising in Tetrolet...

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5th international conference on Information Systems and computer Networks, ISCON 2021

作者： Diwakar, Manoj Pandey, Richa Sharma, Ritik Saun, Sapna Singh, Prabhishek Pandey, Neeraj Kumar Graphic Era Deemed to Be University Department of Computer Science Engineering Uttarakhand Dehradun India Graphic Era Hill University Campus-Haldwani Department of Computer Science Engineering Uttarakhand India Amity University Uttar Pradesh Amity School of Engineering Technology Department of Computer Science Engineering Noida India DIT University School of Computing Dehradun India

ISBN: (纸本)9781665403412

the Internet of medical things (IoMT) is a huge community of linked medical equipment and technologies that communicate with a large number of Servers over the internet to provide a variety of services such as medical image denoising. Projection-based reconstructed images in computed tomography (CT) are noisy due to thermal noise and electrical noise, both of which are approximately AWGN in magnitude. Because of the variety of clinical features and textures included in CT reconstructed images, denoising is a difficult process. It is possible to estimate the true noise of a single image, and then to minimize both the real noise and the additional noise by reconstructing CT images. A unique modified approach is proposed in this work, in which the noise will be calculated actual as well as added, followed by a proposed method for denoising, which is based on thresholding in tetrolet domain. Different denoising schemes are used in the experiments, and the results are compared to one another. Experimentation has revealed that both in terms of PSNR and visual quality, the suggested technique yields promising results that are favorable to both. © 2021 IEEE.

关键词： image denoising

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