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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Chinoy, Danesh Dinyar Mavi, Renu Shrivastav, A.K. Jothi, Naresh Kumar Utsab, Tuhin Rajput, Akshay D y Patil Deemed to Be University School of Physiotherapy Dept of Kinesio Therapeutics and Diagnosis Department of Mathematics Department of Chemistry Navi Mumbai India Swami Vivekananda Subharti University Department of Chemistry Uttar Pradesh Meerut India Mmec Maharshi Markandashwar Department of Mathematics Haryana Ambala India R&d Institute of Science and Technology Department of Mathematics India St. Xavier's University Kolkata India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350383522

Mini-invasive digestive surgery was seemingly one of the main disciplines where soft tissues were handled utilizing test systems, anatomical structure tracking, and specialized assistance robots. Yet, other therapeutic areas, such as urology, are concerned. Unquestionably, laparoscopic surgery, creative growth killing methods (e.g., HIFU, radiofrequency, or cryoablation), prior disease distinguishing proof, and the usage of interventional and diagnostic imaging modalities have of late acquainted new troubles with urologists and researchers keen on CAMI. This has driven in a significant flood in innovative work of computer-aided urology frameworks throughout recent years. In this study, we give a conversation of the main pressing issues related with computer-aided diagnostic and therapy of soft tissues, as well as an overview of the many kinds of help accessible to urologists: robotization, picture combination, and careful route. There is conversation of both exploration projects and functional modern frameworks. © 2024 IEEE.

关键词： Computer Aided medical Interventions medical image computing

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A review on AI-based medical image computing in head and neck surgery

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PHYSICS IN MEDICINE AND BIOLOGY 2022年第17期67卷 17TR01-17TR01页

作者： Xu, Jiangchang Zeng, Bolun Egger, Jan Wang, Chunliang Smedby, Orjan Jiang, Xiaoyi Chen, Xiaojun Shanghai Jiao Tong Univ Sch Mech Engn Inst Biomed Mfg & Life Qual Engn State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Hosp Essen Inst Artificial Intelligence Med Girardetstr 2 D-45131 Essen Germany KTH Royal Inst Technol Dept Biomed Engn & Hlth Syst Stockholm Sweden Univ Munster Fac Math & Comp Sci Munster Germany

Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. medical image computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, artificial intelligence, especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on medical Imaging, medical image Analysis, Physics in Medicine and Biology, medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and eight references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image ana

关键词： medical image computing head and neck deep learning artificial intelligence (AI) convolutional neural network (CNN)

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Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience

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medical image ANALYSIS 2016年第0期33卷 176-180页

作者： Kapur, Tina Pieper, Steve Fedorov, Andriy Fillion-Robin, J-C Halle, Michael O'Donnell, Lauren Lasso, Andras Ungi, Tamas Pinter, Csaba Finet, Julien Pujol, Sonia Jagadeesan, Jayender Tokuda, Junichi Norton, Isaiah Estepar, Raul San Jose Gering, David Aerts, Hugo J. W. L. Jakab, Marianna Hata, Nobuhiko Ibanez, Luiz Blezek, Daniel Miller, Jim Aylward, Stephen Grimson, W. Eric L. Fichtinger, Gabor Wells, William M. Lorensen, William E. Schroeder, Will Kikinis, Ron Brigham & Womens Hosp Boston MA 02115 USA Harvard Med Sch Boston MA 02115 USA Isomics Inc Cambridge MA USA Kitware Inc Clifton Pk NY USA Queens Univ Kingston ON K7L 3N6 Canada Healthmyne Inc Madison WI USA Google New York NY USA Mayo Clin Rochester MN USA Gen Elect Res Garching Germany MIT Cambridge MA 02139 USA

The National Alliance for medical image computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools-VTK, ITK, CMake, CDash, DCMTK-were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today;how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science");and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Open access medical image computing Reproducible research 3D Slicer Hackathon Project week NA-MIC Open source Open science

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medical image computing in diagnosis and intervention of spinal diseases

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COMPUTERIZED medical IMAGING AND GRAPHICS 2015年 45卷 99-101页

作者： Zheng, Guoyan Li, Shuo Univ Bern ISTB CH-3014 Bern Switzerland Univ Western Ontario London ON Canada Digital Imaging Grp London London ON Canada Lawson Hlth Res Inst London ON Canada

Spinal image analysis and computer assisted intervention have emerged as new and independent research areas, due to the importance of treatment of spinal diseases, increasing availability of spinal imaging, and advances in analytics and navigation tools. Among others, multiple modality spinal image analysis and spinal navigation tools have emerged as two keys in this new area. We believe that further focused research in these two areas will lead to a much more efficient and accelerated research path, avoiding detours that exist in other applications, such as in brain and heart. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Spine medical image computing Multiple modality spine image analysis Computer assisted intervention Spinal navigation

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Motility Analysis of Diaphragm in Patients With Chronic Pulmonary Lung Disease Based on Computed Tomography Technique

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IEEE ACCESS 2023年 11卷 101544-101555页

作者： Yu, Hao Nnamdi, Nnubia Pascal Seo, Aria Park, Jinkyeong Son, Yunsik Dongguk Univ Dept Comp Sci & Engn Seoul 04620 South Korea Kyung Hee Univ Kyung Hee Univ Hosp Gangdong Dept Pulm Allergy & Crit Care Med Coll Med Seoul 05278 South Korea

The diaphragm plays a crucial role in respiration, and diaphragm dysfunction is common in COPD, contributing to worsening symptoms and higher mortality rates. Many methods have been implemented to evaluate the function of the diaphragm, but each comes with its unique set of limitations. To overcome this challenge, a novel approach was introduced to assess diaphragm function in patients with chronic obstructive pulmonary disease (COPD) using thoracic computed tomography scans. This novel approach involves generating a simulation of the diaphragm from lung DICOM slice images and calculating the dynamic change in respiratory motion by quantifying the difference in the simulated diaphragm mesh area during inhalation and exhalation. The experimental design incorporates various image processing algorithms, computational geometry algorithms, and a surface fitting algorithm optimized to minimize the potential sources of error. When the proposed technique was applied to detect diaphragmatic dysfunction in patients with COPD, the results of the Pearson correlation analysis showed a strong relationship between the variables ratio of exhalation to inhalation surface and average z-value difference inhalation and exhalation surfaces, with a coefficient of 0.731. The results suggest that the proposed technique is highly accurate and beneficial in scenarios where diaphragm function is essential, such as respiratory disorders, neuromuscular diseases like Amyotrophic Lateral Sclerosis or muscular dystrophy, spinal cord injuries, and conditions such as sleep apnea. By providing specific numerical data on sensitivity and specificity, this approach offers a quantitative evaluation of its effectiveness in detecting diaphragmatic dysfunction in patients with COPD. In conclusion, the proposed approach is currently semi-automated, however, future study may investigate fully automated approaches using this technique.

关键词： Chronic obstructive pulmonary disease computed tomography diaphragm segmentation digital image processing medical image computing

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CONTOUR-WEIGHTED LOSS FOR CLASS-IMBALANCED image SEGMENTATION 31

CONTOUR-WEIGHTED LOSS FOR CLASS-IMBALANCED IMAGE SEGMENTATIO...

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2024 International Conference on image Processing

作者： Huang, Zhengyong Sui, Yao Peking Univ Natl Inst Hlth Data Sci Beijing Peoples R China Peking Univ Inst Med Technol Beijing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

image segmentation is critically important in almost all medical image analysis for automatic interpretations and processing. However, it is often challenging to perform image segmentation due to data imbalance between intra- and inter-class, resulting in over- or under-segmentation. Consequently, we proposed a new methodology to address the above issue, with a compact yet effective contour-weighted loss function. Our new loss function incorporates a contour-weighted cross-entropy loss and separable dice loss. The former loss extracts the contour of target regions via morphological erosion and generates a weight map for the cross-entropy criterion, whereas the latter divides the target regions into contour and non-contour components through the extracted contour map, calculates dice loss separately, and combines them to update the network. We carried out abdominal organ segmentation and brain tumor segmentation on two public datasets to assess our approach. Experimental results demonstrated that our approach offered superior segmentation, as compared to several state-of-the-art methods, while in parallel improving the robustness of those popular state-of-the-art deep models through our new loss function. The code is available at https://***/huangzyong/Contour-weighted-Loss-Seg.

关键词： image segmentation medical image computing contour weighting map deep learning loss function

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AI Driven IoT Web-Based Application for Automatic Segmentation and Reconstruction of Abdominal Organs from medical images 18

AI Driven IoT Web-Based Application for Automatic Segmentati...

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18th Annual International Conference on Distributed computing in Sensor Systems (DCOSS)

作者： Villarini, Barbara Asaturyan, Hykoush A. Univ Westminster Sch Comp Sci & Engn London England

ISBN: (纸本)9781665495127

medical imaging technology has rapidly advanced in the last few decades, providing detailed images of the human body. The accurate analysis of these images and the segmentation of anatomical structures can produce significant morphological information, provide additional guidance toward subject stratification after diagnosis or before a clinical trial, and help predict a medical condition. Usually, medical scans are manually segmented by expert operators, such as radiologists and radiographers, which is complex, time-consuming and prone to inter-observer variability. A system that generates automatic, accurate quantitative organ segmentation on a large scale could deliver a clinical impact, supporting current investigations in subjects with medical conditions and aiding early diagnosis and treatment planning. This paper proposes a web-based application that automatically segments multiple abdominal organs and muscle, produces respective 3D reconstructions and extracts valuable biomarkers using a deep learning backend engine. Furthermore, it is possible to upload image data and access the medical image segmentation tool without installation using any device connected to the Internet. The final aim is to deliver a webbased image-processing service that clinical experts, researchers and users can seamlessly access through IoT devices without requiring knowledge of the underpinning technology.

关键词： IoT Web Application Deep Learning 3D Reconstruction Web Technology medical image computing Organ Segmentation

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Moving medical image Analysis to GPU Embedded Systems: Application to Brain Tumor Segmentation

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APPLIED ARTIFICIAL INTELLIGENCE 2020年第12期34卷 866-879页

作者： Niepceron, Brad Nait-Sidi-Moh, Ahmed Grassia, Filippo Univ Picardie Jules Verne Lab Innovat Technol LTI EA 3899 F-80025 Amiens France

With the growth of medical data stored as bases for researches and diagnosis tasks, healthcare providers are in need of automatic processing methods to make accurate and fast image analysis such as segmentation or restoration. Most of the existing solutions to deal with these tasks are based on Deep Learning methods that require the use of powerful dedicated hardware to be executed and address a power consumption problem that is not compatible with the aforementioned requests. There is thus a demand in the development of low-cost image analysis systems with increased performances. In this work, we address this problem by proposing a fully-automatic brain tumor segmentation method based on a Convolutional Neural Network, executed by a low-cost, Deep Learning ready GPU embedded platform. We validated our approach using the BRaTS 2015 dataset to segment brain tumors and proved that an artificial neural network can be trained and used in the medical field with limited resources by redefining some of its inner operations.

关键词： Convolutional neural network Graphics processing unit medical image computing Segment Deep learning image analysis

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medical imaging and computational image analysis in COVID-19 diagnosis: A review

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COMPUTERS IN BIOLOGY AND MEDICINE 2021年 135卷 104605-104605页

作者： Nabavi, Shahabedin Ejmalian, Azar Moghaddam, Mohsen Ebrahimi Abin, Ahmad Ali Frangi, Alejandro F. Mohammadi, Mohammad Rad, Hamidreza Saligheh Shahid Beheshti Univ Fac Comp Sci & Engn Tehran Iran Shahid Beheshti Univ Med Sci Anesthesiol Res Ctr Tehran Iran Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed CIS Sch Comp Leeds W Yorkshire England Royal Adelaide Hosp Dept Med Phys Adelaide SA Australia Univ Adelaide Sch Phys Sci Adelaide SA Australia Univ Tehran Med Sci Quantitat MR Imaging & Spect Grp QMISG Tehran Iran

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learningbased methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

关键词： Computed tomography Corona virus COVID-19 Deep learning Machine learning medical image computing medical imaging

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TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 208卷 106236-106236页

作者： Perez-Garcia, Fernando Sparks, Rachel Ourselin, Sebastien UCL Dept Med Phys & Biomed Engn London England UCL Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London England Kings Coll London Sch Biomed Engn & Imaging Sci BMEIS London England

Background and objective: Processing of medical images such as MRI or CT presents different challenges compared to RGB images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and the need of metadata to describe the physical properties of voxels. Data augmentation is used to artificially increase the size of the training datasets. Training with image subvolumes or patches decreases the need for computational power. Spatial metadata needs to be carefully taken into account in order to ensure a correct alignment and orientation of volumes. Methods: We present TorchIO, an open-source Python library to enable efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. TorchIO follows the style of PyTorch and integrates standard medical image processing libraries to efficiently process images during training of neural networks. TorchIO transforms can be easily composed, reproduced, traced and extended. Most transforms can be inverted, making the library suitable for test-time augmentation and estimation of aleatoric uncertainty in the context of segmentation. We provide multiple generic preprocessing and augmentation operations as well as simulation of MRI-specific artifacts. Results: Source code, comprehensive tutorials and extensive documentation for TorchIO can be found at http://***/ . The package can be installed from the Python Package Index (PyPI) running pip install torchio . It includes a command-line interface which allows users to apply transforms to image files without using Python. Additionally, we provide a graphical user interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms. Conclusion: TorchIO was developed to help researchers standardize medical image processing pipelines and allow them to focus on the deep learning experiments. It encourages good open-science practices, as it supports experiment r

关键词： medical image computing Deep learning Data augmentation Preprocessing

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