深度学习在癌症图像分割中的应用

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of Medical Radiation Sciences Pub Date : 2024-11-06 DOI:10.1002/jmrs.839

Robba Rai BAppSc (DiagRad), MHlthSc (MRI), PhD

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摘要

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Deep learning in image segmentation for cancer

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Deep learning in image segmentation for cancer

This article discusses the role of deep learning (DL) in cancer imaging, focusing on its applications for automatic image segmentation. It highlights two studies that demonstrate how U-Net- and convolutional neural networks–based architectures have improved the speed and accuracy of body composition analysis in CT scans and rectal tumour segmentation in MRI images. While the results are promising, the article stresses the need for further research to address issues like image quality variability across different imaging systems.

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来源期刊

Journal of Medical Radiation Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

3.20

自引率

4.80%

发文量

审稿时长

8 weeks

期刊介绍： Journal of Medical Radiation Sciences (JMRS) is an international and multidisciplinary peer-reviewed journal that accepts manuscripts related to medical imaging / diagnostic radiography, radiation therapy, nuclear medicine, medical ultrasound / sonography, and the complementary disciplines of medical physics, radiology, radiation oncology, nursing, psychology and sociology. Manuscripts may take the form of: original articles, review articles, commentary articles, technical evaluations, case series and case studies. JMRS promotes excellence in international medical radiation science by the publication of contemporary and advanced research that encourages the adoption of the best clinical, scientific and educational practices in international communities. JMRS is the official professional journal of the Australian Society of Medical Imaging and Radiation Therapy (ASMIRT) and the New Zealand Institute of Medical Radiation Technology (NZIMRT).