Machine learning approaches to analyze histological images of tissues from radical prostatectomies
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- 作者：Arkadiusz Gertycha ; b ; arkadiusz.gertych@cshs.org" class="auth_mail" title="E-mail the corresponding author ; agertych@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Nathan Inga ; Zhaoxuan Mac ; Thomas J. Fuchsd ; e ; f ; Sadri Salmana ; Sambit Mohantyb ; Sanica Bheleb ; Adriana Velá ; squez-Vaccaa ; Mahul B. Aminb ; Beatrice S. Knudsenb ; c
- 关键词：Machine learning ; Image analysis ; Prostate cancer ; Tissue classification ; Tissue quantification
- 刊名：Computerized Medical Imaging and Graphics
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：46
- 期：part_P2
- 页码：197-208
- 全文大小：4435 K
文摘
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Machine learning approaches were applied to separate stroma from epithelium in prostate tissue images.
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Epithelium was sub-stratified into normal/benign and cancer areas.
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Tissue content was predicted based on descriptors from individual pixels rather than from glands.
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Tissue prediction does not involve detection of glandular lumens which is inaccurate, prone to errors, and has limitations.
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Proposed method has the potential to aid in clinical prostate studies.