9 December 2016 Brain tumor classification of microscopy images using deep residual learning
Author Affiliations +
Abstract
The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yota Ishikawa, Yota Ishikawa, Kiyotada Washiya, Kiyotada Washiya, Kota Aoki, Kota Aoki, Hiroshi Nagahashi, Hiroshi Nagahashi, } "Brain tumor classification of microscopy images using deep residual learning", Proc. SPIE 10013, SPIE BioPhotonics Australasia, 100132Y (9 December 2016); doi: 10.1117/12.2242711; https://doi.org/10.1117/12.2242711
PROCEEDINGS
10 PAGES


SHARE
Back to Top