Histological Images Processing and Analysis

This page is devoted to our research on different problems of histological images processing and analysis.

Our research is focused on developing algorithms for:

  • semantic segmentation of mucous glands in histological images;
  • instance segmentation of mucous glands in histological images;
  • hybrid approach of instance gland segmentation.

Datasets

PATH-DT-MSU

In our research we use PATH-DT-MSU dataset, that was collected and prepared by Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University and Department of Pathology, Medical Research and Educational Center (University Clinic), Lomonosov Moscow State University.

sample image from S1 set visualization of corresponding gland and "open" gland annotations from S1 set preview of whole slide image from WSS2 with visualization of annotation

PathScribe project

PathScribe is a multi-platform client-server application that allows to efficiently work with whole slide images of any size without having to download them to your device. PathScribe implements an adaptive and modern interface that allows to comfortably work with histological images, both on a desktop computer or laptop, and on mobile devices (tablets, smartphones) with touch screens.

Website: https://pathscribe.ru/

Our team

Alexander Khvostikov
khvostikov@cs.msu.ru
ORCID: 0000-0002-4217-7141
PhD, Researcher, Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University

Andrey Krylov
kryl@cs.msu.ru
ORCID: 0000-0001-9910-4501
Professor, Head of the Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University

Ilya Mikhailov
imihailov@mc.msu.ru
ORCID: 0000-0001-8020-369X
PhD, Trainee researcher, Department of Pathology, Medical Research and Educational Center, Lomonosov Moscow State University.

Nina Oleynikova
noleynikova@mc.msu.ru
ORCID: 0000-0001-8564-8874
MD, PhD, Researcher scientist, Department of Pathology, Medical Research and Educational Center, Lomonosov Moscow State University.

Olga Kharlova
olga.arsenteva@gmail.com
ORCID: 0000-0002-5909-1248
MD, PhD

Natalia Danilova
ndanilova@mc.msu.ru
ORCID: 0000-0001-7848-6707
MD, PhD, Senior researcher scientist, Department of Pathology, Medical Research and Educational Center, Lomonosov Moscow State University.

Pavel Malkov
pmalkov@mc.msu.ru
ORCID: 0000-0001-5074-3513
MD, ScD, Head of Department of Pathology, Medical Research and Educational Center, Lomonosov Moscow State University.

Bibliography

2023

  • Alexander Khvostikov, Andrey Krylov, Ilya Mikhailov, and Pavel Malkov. Visualization and analysis of whole slide histological images. Lecture Notes in Computer Science, 13644:403–413, 2023. DOI
  • V. E. Karnaukhov, A. V. Khvostikov, and A. S. Krylov. Generative augmentation methods for histological image analysis in limited data conditions. Computational Mathematics and Modeling, 33(3):365–374, 2023. DOI
  • M. A. Penkin, A. V. Khvostikov, and A. S. Krylov. Automated method for optimum scale search when using trained models for histological image analysis. Programming and Computer Software, 49(3):172–177, 2023. DOI
  • N. D. Lokshin, A. V. Khvostikov, and A. S. Krylov. Augmenting the training set of histological images with adversarial examples. Programming and Computer Software, 49(3):187–191, 2023. DOI
  • A. S. Veshkin and A. V. Khvostikov. Multiscale content-based image retrieval for whole-slide histological images. Computational Mathematics and Modeling, 33(2):244–254, 2023. DOI
  • Sun Zhongao, Alexander Khvostikov, Andrey Krylov, and Nikolai Krainiukov. Super-resolution for whole slide histological images. In Proceedings of the 33nd International Conference on Computer Graphics and Vision, pages 609–619, Moscow, 2023. DOI
  • Nikita Yakovlev, Alexander Khvostikov, and Andrey Krylov. Method for automatic initialization of trainable active contours for instance segmentation in histological images. In Proceedings of the 33nd International Conference on Computer Graphics and Vision, pages 598–608, Moscow, 2023. DOI

2022

  • I. A. Mikhailov, A. V. Khvostikov, and A. S. Krylov. Methodical approaches to annotation and labeling of histological images in order to automatically detect the layers of the stomach wall and the depth of invasion of gastric cancer. Pathology Archive, 84(6):67–73, 2022. DOI
  • A. V. Khvostikov, A. S. Krylov, I. A. Mikhailov, and P. G. Malkov. Visualization of whole slide histological images with automatic tissue type recognition. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, 32(3):483–488, 2022. DOI
  • O. L. Pochernina, A. V. Khvostikov, and A. S. Krylov. Semi-automatic algorithm for lumen segmentation in histological images. In Proceedings of the 32nd International Conference on Computer Graphics and Vision, pages 648–656, Moscow, 2022. DOI

2021

  • Alexander Khvostikov, Andrey Krylov, Ilya Mikhailov, Pavel Malkov, and Natalya Danilova. Tissue type recognition in whole slide histological images. CEUR Workshop Proceedings, 3027:50, 2021. DOI
  • I. Mikhailov, A. Khvostikov, Andrey S. Krylov, P. Malkov, N. Danilova, and N. Oleynikova. Development of CNN-based algorithm for automatic recognition of the layers of the wall of the stomach and colon. Virchows Archiv, 479(Suppl 1):OFP–15–004, 2021. DOI

2020

  • I. Mikhailov, A. Khvostikov, Andrey S. Krylov, P. Malkov, N. Oleynikova, O. Kharlova, and N. Danilova. Development of semi-automatic interactive algorithm for annotating histological images of colon epithelial neoplasms. Virchows Archiv, 477(S1):OFP–10–002, 2020. DOI
  • Alexander Khvostikov, Andrey Krylov, Ilya Mikhailov, and Pavel Malkov. CNN assisted hybrid algorithm for medical images segmentation. In ICBIP '20: Proceedings of the 2020 5th International Conference on Biomedical Signal and Image Processing, pages 14–19, New York, N.Y., United States, 2020. ACM. DOI

2019

  • A. V. Khvostikov, A. S. Krylov, I. A. Mikhailov, and P. G. Malkov. Trainable active contour model for histological image segmentation. Scientific Visualization, 11(3):64–75, 2019. DOI
  • A. V. Khvostikov, A. S. Krylov, I. A. Mikhailov, O. A. Kharlova, N. A. Oleynikova, and P. G. Malkov. Automatic mucous glands segmentation in histological images. ISPRS Journal of Photogrammetry and Remote Sensing, 42(2/W12):103–109, 2019. DOI
  • N. Oleynikova, A. Khvostikov, A. S. Krylov, I. Mikhailov, O. Kharlova, N. Danilova, P. Malkov, N. Ageykina, and E. Fedorov. Automatic glands segmentation in histological images obtained by endoscopic biopsy from various parts of the colon. Endoscopy, 51(4):S6–S7, 2019. DOI