Unsupervised segmentation and data augmentation in image sequences of skeletal muscle contraction by cycle-consistent generative adversarial network

This paper investigates a method addressing the unsupervised segmentation and joint data augmentation in medical ultrasound imaging based on the modified CycleGAN. Accurate quantification of fascia and muscle is the key for the diagnostics of neuromuscular disorders based on the analysis of image sequences of skeletal muscle contraction. Although the Deep Learning (DL) models represent encouraging results, some challenges exist. The traditional models don't consider the complex interaction between tissues within a muscle and its surroundings, which reduces the performance of the fascia segmentation. Also, the DL requires many annotated datasets, which ignores dealing with noisy and complex ultrasound images. To overcome these issues, we propose a method to generate realistic images, and then present an unsupervised fascia segmentation method. The results show that our method improves the segmentation accuracy in noisy and complex ultrasound images compared to the traditional methods.

Grönlund, ChristerUmeå universitet,Institutionen för strålningsvetenskaper(Swepub:umu)chgr0009 (author)
Umeå universitetInstitutionen för strålningsvetenskaper (creator_code:org_t)

In:2023 international conference on modeling, simulation & intelligent computing (MoSICom): IEEE, s. 474-47997983503934159798350393422

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Medical Engineer ...; and Medical Image Pr ...

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Clinical Medicin ...; and Radiology Nuclea ...

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Vision ...

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