Automatic histopathology image analysis with CNNs
Le Hou, K. Ranjith Singh, Dimitris Samaras, Tahsin Kurç, Yi Gao, Roberta Seidman, Joel Haskin Saltz · 2016
We define Pathomics as the process of high throughput generation, interrogation, and mining of quantitative features from high-resolution histopathology tissue images. Analysis and mining of large volumes of imaging features has great potential to enhance our understanding of tumors. The basic Pathomics workflow consists of several steps: segmentation of tissue images to delineate the boundaries of nuclei, cells, and other structures; computation of size, shape, intensity, and texture features for each segmented object; classification of images and patients based on imaging features; and correlation of classification results with genomic signatures and clinical outcome. Executing a Pathomics workflow on a dataset of thousands of very high resolution (gigapixels) and heterogeneous histopathology images is a computationally challenging problem. In this paper, we use Convolutional Neural Networks (CNN) for automatic recognition of nuclear morphological attributes in histopathology images of glioma, the most common malignant brain tumor. We constructed a comprehensive multi-label dataset of glioma nuclei and applied two CNN based methods on this dataset. Both methods perform well recognizing some but not all morphological attributes and are complementary with each other.