A combined learning algorithm for prostate segmentation on 3D CT images-Reference-Cited by-同舟云学术

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A combined learning algorithm for prostate segmentation on 3D CT images

Published:2017-09-22 Issue:11 Volume:44 Page:5768-5781
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Ma Ling¹,Guo Rongrong¹,Zhang Guoyi¹,Schuster David M.¹,Fei Baowei¹²³⁴

Affiliation:

1. Department of Radiology and Imaging Sciences; Emory University School of Medicine; Atlanta GA USA

2. Department of Biomedical Engineering; Emory University and Georgia Institute of Technology; Atlanta GA USA

3. Winship Cancer Institute of Emory University; Atlanta GA USA

4. Department of Mathematics and Computer Science; Emory University; Atlanta GA USA

Funder

National Institutes of Health

Publisher

Wiley

Subject

General Medicine

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/mp.12528/fullpdf

Reference35 articles.

1. Cancer statistics, 2016;Siegel;CA Cancer J Clin,2015

2. A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images;Ghose;Comput Meth Programs Biomed,2012

3. Evaluation of multi-atlas-based segmentation of CT scans in prostate cancer radiotherapy

4. A geodesic deformable model for automatic segmentation of image sequences applied to radiation therapy;Bueno;Int J Comput Assist Radiol Surg,2011

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2. Research related to the diagnosis of prostate cancer based on machine learning medical images: A review;International Journal of Medical Informatics;2024-01

3. Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions;Diagnostics;2023-03-03

4. PSA-Net: Deep learning–based physician style–aware segmentation network for postoperative prostate cancer clinical target volumes;Artificial Intelligence in Medicine;2021-11

5. HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation in CT Images;IEEE Transactions on Medical Imaging;2021-08

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