Year: 2025
Type: article
Abstract: Brain tumour segmentation is critical in medical image analysis, facilitating diagnosis and treatment planning in neurosurgery. Brain tumour segmentation with supervised learning shows robust results in medical imaging; however, it requires a sufficient amount of annotated data for effective learning. It is important to detect boundaries of tumour subregions accurately in fine-grained segmentation. We propose a novel approach that uses a unique dual-decoder architecture, focusing on edge identification and segmentation accuracy enhancement. Utilising a dual-decoder 3D-UNet model, we prioritise accuracy and fine-grained details in tumour segmentation and introduce an additional tumour edge detection task, aiming to move beyond traditional single-decoder approaches. Incorporating a 3D SimSiam network as the self-supervised pretraining technique, we aim to address the limitation of annotated data and enhance the segmentation accuracy. Our model surpasses many supervised variants of U-net architectures and self-supervised approaches, highlighting the importance of edge detection in tumour segmentation. The proposed approach enhances segmentation accuracy by showing an accuracy of 98.1% and provides critical boundary details for clinical decision-making. Visualisations of segmentation and edge masks further validate the effectiveness of the proposed method.
Authors: Dasith Samarasinghe, Deshan Wickramasinghe, Theshan Wijerathne, Dulani Meedeniya, Pratheepan Yogarajah
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $312
OpenAlex ID: W4409565216