Automatic Segmentation of Achilles Tendon Tissues Using Deep Convolutional Neural Network

Tariq Alzyadat, Stephan Praet, Girija Chetty, Roland Goecke, David Hughes, Dinesh Kumar, Marijke Welvaert, Nicole Vlahovich, Gordon Waddington

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

2 Citations (Scopus)


The automatic segmentation of Achilles tendon tissues is one of the preliminary steps towards creating a tool for diagnosing, prognosing, or monitoring changes in tendon organization over time. Manual delineation is the current approach of identifying Achilles region-of-interest (ROI), it is a tedious and time-consuming task. In this respect, the current work describes the first steps taken towards creating an automatic approach for Achilles tendon segmentation that utilize the capabilities of Deep Convolutional Neural Networks (CNNs). Firstly, the dataset has been pre-processed and manually segmented to be used as the ground-truth in the training and testing of the proposed automated model. Secondly, the model was trained and validated using three CNN architectures SegNet, ResNet-18 and ResNet-50. Finally, Tversky loss function, 3D augmentation and network ensembling approaches were used to improve the segmentation performance and to tackle challenges such as the limited size of the training dataset and data imbalance. The proposed fully automated segmentation method reached average Dice score of 0.904. In conclusion, this novel study demonstrates that a CNN approach is useful for performing accurate Achilles tendon segmentation in musculoskeletal imaging.
Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsMingxia Liu, Pingkun Yan, Chunfeng Lian, Xiaohuan Cao
Place of PublicationNetherlands
Number of pages11
ISBN (Electronic)9783030598617
ISBN (Print)9783030598600
Publication statusPublished - 1 Jan 2020
EventMachine Learning in Medical Imaging (MLMI 2020): In conjunction with MICCAI 2020 - Lima, Lima, Peru
Duration: 4 Oct 20204 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12436 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


WorkshopMachine Learning in Medical Imaging (MLMI 2020)
Abbreviated titleMLMI 2020
Internet address


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