Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches - Kurc, Tahsin; Bakas, Spyridon; Ren, Xuhua; Bagari, Aditya; Momeni, Alexandre; Huang, Yue; Zhang, Lichi; Kumar, Ashish; Thibault, Marc; Qi, Qi; Wang, Qian; Kori, Avinash; et al. (Frontiers in Neuroscience, 2020)
Biomedical imaging is an important source of information in cancer research. Characterizations of cancer morphology and function at onset, progression and in response to treatment provide complementary information to that gleaned from genomics and clinical data. Accurate extraction and classification of both visual and latent imaging features has been an increasingly complex challenge due to the increased complexity and resolution of imaging data. In this paper, we present image analysis methods that employ deep learning approaches to i) segment nuclei in whole slide images of glioma and ii) classify brain tumor cases as either Oligodendroglioma or Astrocytoma using radiographic and histologic imaging data. These methods were submitted to the Computational Precision Medicine satellite event at the 21st International Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018 conference and achieved top scores; 0.87 Dice Similarity Coefficient in segmentation and 0.75-0.90 Accuracy in classification. The results indicate that carefully constructed deep learning networks are able to produce high accuracy in biomedical image analysis and the combination of radiographic with histologic image information improves classification performance.