Joint Capsule Segmentation in Ultrasound Images of the Metacarpophalangeal Joint using Convolutional Neural Networks

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Martins, N (Author) Other; Costa, E (Author) Other; Veiga, D (Author) Other; Ferreira, M (Author) Other; Coimbra, M (Author) FCUP

Conference proceedings International

Title: 6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019 - Proceedings

6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019

22 February 2019 through 23 February 2019

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-00Q-K19

DOI: 10.1109/enbeng.2019.8692489

Abstract (EN): This work addresses the automatic segmentation of the joint capsule in ultrasound images of the metacarpophalangeal joint using an adapted version of the well known UNet model. These images are used in the diagnosis of rheumatic diseases, one of the main causes of impairment and pain in developed countries. The identification of the joint capsule gives important clues about the presence or Rheumatoid Arthritis. This structure can be used to extract metrics to help quantify the disease stage and progression. The solution proposed here has the potential to reduce the burden on the radiologists as well as the subjectivity of the diagnosis by providing quantitative measurements, such as the synovitis area. The proposed approach was compared with two other works present in the literature. Results show that our solution outperforms the two reference methods with 90% of the joint capsules identified with a DICE higher than 0.67.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4