Title: Atherosclerotic plaque segmentation using modified UNet with hybrid pooling layers

Authors: Soni Singh; Pankaj Kumar Jain; Neeraj Sharma; Mausumi Pohit

Addresses: School of Vocational Studies and Applied Sciences, Gautam Buddha University, Greater Noida, UP, India ' School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, UP, India ' School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, UP, India ' School of Vocational Studies and Applied Sciences, Gautam Buddha University, Greater Noida, UP, India

Abstract: Atherosclerotic plaque segmentation is a vital task in cardiovascular image processing. Fuzzy nature of the carotid images makes it difficult to extract vital features from the plaque tissue region. UNet deep learning models use max-pooling layers for extraction of feature maps and are quite effective in medical image segmentation. In this study, we hypothesised that the UNet model with a hybrid pooling layer consisting of average pooling layer and max-pooling layers could exert more control on feature selection, and therefore be more effective solution for carotid plaque segmentation. We used a public database of 66 B-mode ultrasound images of the carotid artery for our experiments. We experimented with four cases of modified UNet model using a hybrid pooling layer with four different values of 'α' and compared it with the standard UNet model. Modified UNet model with hybrid pooling layers shows nearly 5% improvements in DSC and JI values.

Keywords: UNet; hybrid pooling layer; atherosclerosis; carotid plaque segmentation; deep learning.

DOI: 10.1504/IJBET.2024.137344

International Journal of Biomedical Engineering and Technology, 2024 Vol.44 No.3, pp.205 - 225

Received: 16 Jul 2022
Accepted: 10 Feb 2023
Published online: 13 Mar 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article