Volume XLII-2/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 103-109, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-103-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 103-109, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-103-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  09 May 2019

09 May 2019

AUTOMATIC MUCOUS GLANDS SEGMENTATION IN HISTOLOGICAL IMAGES

A. Khvostikov1, A. Krylov1, I. Mikhailov2, O. Kharlova2, N. Oleynikova2, and P. Malkov2 A. Khvostikov et al.
  • 1Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia
  • 2University Medical Center, Lomonosov Moscow State University, Moscow, Russia

Keywords: Image segmentation, Mucous glands, Deep Learning, Convolutional Neural Networks, Histology, Pathology

Abstract. Mucous glands is an important diagnostic element in digestive pathology. The first step of differential diagnosis of colon polyps in order to assess their malignant potential is gland segmentation. The process of mucous glands segmentation is challenging as the glands not only needed to be separated from a background but also individually identified to obtain reliable morphometric criteria for quantitative diagnostic methods. We propose a new convolutional neural network for mucous gland segmentation that takes into account glands’ contours and can be used for gland instance segmentation. Training and evaluation of the network was performed on a standard Warwick-QU dataset as well as on the collected PATH-DT-MSU dataset of histological images obtained from hematoxylin and eosin staining of paraffin sections of colon biopsy material collected by our Pathology department. The collected PATH-DT-MSU dataset will be available at http://imaging.cs.msu.ru/en/research/histology.