Optimized color decomposition of localized whole slide images and convolutional neural network for intermediate prostate cancer classification

Naiyun Zhou; Yi Gao

doi:10.1117/12.2254216

1 March 2017 Optimized color decomposition of localized whole slide images and convolutional neural network for intermediate prostate cancer classification

Naiyun Zhou, Yi Gao

Author Affiliations +

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 101400W (2017) https://doi.org/10.1117/12.2254216
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

This paper presents a fully automatic approach to grade intermediate prostate malignancy with hematoxylin and eosin-stained whole slide images. Deep learning architectures such as convolutional neural networks have been utilized in the domain of histopathology for automated carcinoma detection and classification. However, few work show its power in discriminating intermediate Gleason patterns, due to sporadic distribution of prostate glands on stained surgical section samples. We propose optimized hematoxylin decomposition on localized images, followed by convolutional neural network to classify Gleason patterns 3+4 and 4+3 without handcrafted features or gland segmentation. Crucial glands morphology and structural relationship of nuclei are extracted twice in different color space by the multi-scale strategy to mimic pathologists’ visual examination. Our novel classification scheme evaluated on 169 whole slide images yielded a 70.41% accuracy and corresponding area under the receiver operating characteristic curve of 0.7247.

Citation Download Citation

Naiyun Zhou and Yi Gao "Optimized color decomposition of localized whole slide images and convolutional neural network for intermediate prostate cancer classification", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400W (1 March 2017); https://doi.org/10.1117/12.2254216

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