دورية أكاديمية
أعرض في EDS

Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey.

التفاصيل البيبلوغرافية
العنوان: Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey.
المؤلفون: Huang SY; Institute of Medical Science, Tzu Chi University, Hualien 97071, Taiwan.; Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan., Hsu WL; Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan.; Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan.; School of Medicine, Tzu Chi University, Hualien 97071, Taiwan., Hsu RJ; Institute of Medical Science, Tzu Chi University, Hualien 97071, Taiwan.; Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan.; School of Medicine, Tzu Chi University, Hualien 97071, Taiwan., Liu DW; Institute of Medical Science, Tzu Chi University, Hualien 97071, Taiwan.; Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan.; Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan.; School of Medicine, Tzu Chi University, Hualien 97071, Taiwan.
المصدر: Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2022 Nov 11; Vol. 12 (11). Date of Electronic Publication: 2022 Nov 11.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101658402 Publication Model: Electronic Cited Medium: Print ISSN: 2075-4418 (Print) Linking ISSN: 20754418 NLM ISO Abbreviation: Diagnostics (Basel) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI AG, [2011]-
مستخلص: There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. This study retrospectively reviews recent studies on the application of deep learning for segmentation tasks in medical imaging and proposes potential directions for future development, including model development, data augmentation processing, and dataset creation. The strengths and deficiencies of studies on models and data augmentation, as well as their application to medical image segmentation, were analyzed. Fully convolutional network developments have led to the creation of the U-Net and its derivatives. Another noteworthy image segmentation model is DeepLab. Regarding data augmentation, due to the low data volume of medical images, most studies focus on means to increase the wealth of medical image data. Generative adversarial networks (GAN) increase data volume via deep learning. Despite the increasing types of medical image datasets, there is still a deficiency of datasets on specific problems, which should be improved moving forward. Considering the wealth of ongoing research on the application of deep learning processing to medical image segmentation, the data volume and practical clinical application problems must be addressed to ensure that the results are properly applied.
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معلومات مُعتمدة: Grant TCRD-110-15, IMAR-110-01-08 Buddhist Tzu Chi Medical Foundation
فهرسة مساهمة: Keywords: deep learning; fully-convolutional network; medical image processing; semantic segmentation
تواريخ الأحداث: Date Created: 20221126 Latest Revision: 20221213
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC9689961
DOI: 10.3390/diagnostics12112765
PMID: 36428824
قاعدة البيانات: MEDLINE
الوصف
تدمد:2075-4418
DOI:10.3390/diagnostics12112765