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

Aleksei Tiulpin, Mikko Finnilä, Petri Lehenkari, Heikki J. Nieminen, Simo Saarakkala

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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 $$\upmu $$m, 30 $$\upmu $$m, 45 $$\upmu $$m, 60 $$\upmu $$m. and 75 $$\upmu $$m 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.

Originalspråkengelska
Titel på värdpublikationAdvanced Concepts for Intelligent Vision Systems : 20th International Conference, ACIVS 2020, Auckland, New Zealand, February 10–14, 2020, Proceedings
RedaktörerJacques Blanc-Talon, Patrice Delmas, Wilfried Philips, Dan Popescu, Paul Scheunders
Antal sidor8
UtgivningsortCham
FörlagSpringer Nature Switzerland AG
Utgivningsdatum2020
Sidor131-138
ISBN (tryckt)978-3-030-40604-2
ISBN (elektroniskt)978-3-030-40605-9
DOI
StatusPublicerad - 2020
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangInternational Conference on Advanced Concepts for Intelligent Vision Systems - Auckland, Nya Zeeland
Varaktighet: 10 feb. 202014 feb. 2020
Konferensnummer: 20
http://acivs.org/acivs2020/

Publikationsserier

NamnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volym12002 LNCS
ISSN (tryckt)0302-9743
ISSN (elektroniskt)1611-3349

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap
  • 114 Fysik
  • 3121 Allmänmedicin, inre medicin och annan klinisk medicin

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