Segmentation of Femoral Cartilage from Knee Ultrasound Images Using Mask R-CNN

Gayatri Kompella, Maria Antico, Fumio Sasazawa, S. Jeevakala, Keerthi Ram, Davide Fontanarosa, Ajay K. Pandey, Mohanasankar Sivaprakasam

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

9 Citations (Scopus)

Abstract

Segmentation of knee cartilage from Ultrasound (US) images is essential for various clinical tasks in diagnosis and treatment planning of Osteoarthritis. Moreover, the potential use of US imaging for guidance in robotic knee arthroscopy is presently being investigated. The femoral cartilage being the main organ at risk during the operation, it is paramount to be able to segment this structure, to make US guidance feasible. In this paper, we set forth a deep learning network, Mask R-CNN, based femoral cartilage segmentation in 2D US images for these types of applications. While the traditional imaging approaches showed promising results, they are mostly not real-time and involve human interaction. This being the case, in recent years, deep learning has paved its way into medical imaging showing commendable results. However, deep learning-based segmentation in US images remains unexplored. In the present study we employ Mask R-CNN on US images of the knee cartilage. The performance of the method is analyzed in various scenarios, with and without Gaussian filter preprocessing and pretraining the network with different datasets. The best results are observed when the images are preprocessed and the network is pretrained with COCO 2016 image dataset. A maximum Dice Similarity Coefficient (DSC) of 0.88 and an average DSC of 0.80 is achieved when tested on 55 images indicating that the proposed method has a potential for clinical applications.

Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
EditorsShankar Subramaniam
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages966-969
Number of pages4
ISBN (Electronic)9781538613115
ISBN (Print)9781538613122
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: 23 Jul 201927 Jul 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Country/TerritoryGermany
CityBerlin
Period23/07/1927/07/19

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