Attention LinkNet-152: a novel encoder-decoder based deep learning network for automated spine segmentation
Year: 2025
Type: article
Abstract: Segmenting the spine from CT images is crucial for diagnosing and treating spine-related conditions but remains challenging due to the spine's complex anatomy and imaging artifacts. This study introduces a novel encoder-decoder-based deep learning approach, named LinkNet-152, tailored for automated spine segmentation. The model integrates a modified EfficientNetB7 encoder with attention modules to enhance feature extraction by focusing on regions of interest. The decoder leverages a modified LinkNet architecture, replacing ResNet34 with the deeper ResNet152 to improve feature extraction and segmentation accuracy. Additionally, gradient sensitivity-based pruning is applied to optimize the model's complexity and computational efficiency. Evaluated on the VerSe 2019 and VerSe 2020 datasets, the proposed model achieves superior performance, with a Dice coefficient of 96.85% and a Jaccard index of 95.37%, outperforming state-of-the-art methods. These results highlight the model's effectiveness in addressing the challenges of spine segmentation and its potential to advance clinical applications.
Source: Scientific Reports
Authors: Aqsa Dastgir, Wang Bin, Muhammad Usman Saeed, Jinfang Sheng, Luo Site, Haseeb Hassan
Institutions: Central South University, Shenzhen Technology University
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G8054101237
OpenAlex ID: W4409506965