Deep-Learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • University of Oulu
  • University of Helsinki
  • University Hospital of Oulu

Abstract

Three-dimensional (3D) semi-quantitative grading of pathological features in articular cartilage (AC) offers significant improvements in basic research of osteoarthritis (OA). We have earlier developed the 3D protocol for imaging of AC and its structures which includes staining of the sample with a contrast agent (phosphotungstic acid, PTA) and a consequent scanning with micro-computed tomography. Such a protocol was designed to provide X-ray attenuation contrast to visualize AC structure. However, at the same time, this protocol has one major disadvantage: the loss of contrast at the tidemark (calcified cartilage interface, CCI). An accurate segmentation of CCI can be very important for understanding the etiology of OA and ex-vivo evaluation of tidemark condition at early OA stages. In this paper, we present the first application of Deep Learning to PTA-stained osteochondral samples that allows to perform tidemark segmentation in a fully-automatic manner. Our method is based on U-Net trained using a combination of binary cross-entropy and soft-Jaccard loss. On cross-validation, this approach yielded intersection over the union of 0.59, 0.70, 0.79, 0.83 and 0.86 within 15 μm, 30 μm, 45 μm, 60 μm, and 75 μm padded zones around the tidemark, respectively. Our codes and the dataset that consisted of 35 PTA-stained human AC samples are made publicly available together with the segmentation masks to facilitate the development of biomedical image segmentation methods.

Details

Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems - 20th International Conference, ACIVS 2020, Proceedings
EditorsJacques Blanc-Talon, Patrice Delmas, Wilfried Philips, Dan Popescu, Paul Scheunders
Publication statusPublished - 1 Jan 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Advanced Concepts for Intelligent Vision Systems - Auckland, New Zealand
Duration: 10 Feb 202014 Feb 2020
Conference number: 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume12002 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Advanced Concepts for Intelligent Vision Systems
Abbreviated titleACIVS
CountryNew Zealand
CityAuckland
Period10/02/202014/02/2020

    Research areas

  • 3D histology, Deep Learning, Osteoarthritis

ID: 41761670