Date of Submission
2025
Date of Award
6-11-2025
Institute Name (Publisher)
Indian Statistical Institute
Document Type
Master's Dissertation
Degree Name
Master of Technology
Subject Name
Computer Science
Department
Computer Vision and Pattern Recognition Unit (CVPR-Kolkata)
Supervisor
Palit, Sarbani
Abstract (Summary of the Work)
Accurate segmentation of lesions in chest CT scans plays a vital role in diagnosing and monitoring pulmonary diseases such as COVID-19. In this, we introduce a novel 2.5D[1] dual-encoder U-Net model[2] that utilizes both the central slice and its neighboring slices to improve segmentation accuracy while keeping computational demands manageable. Our model incorporates residual connections[3] and feature fusion[4] to effectively merge multi-slice contextual information, overcoming the limitations found in traditional 2D and 3D methods. To ensure a reliable evaluation and avoid data leakage, we used patient-level data splitting. We validate our approach on a carefully curated chest CT dataset, showing enhanced segmentation performance and better generalization compared to standard U-Net models. Through extensive experiments, including ablation studies and visualizations, we demonstrate the advantages of combining 2.5D learning with a dual-encoder architecture for medical image segmentation tasks.
Control Number
CS2308
DOI
https://dspace.isical.ac.in/items/01a178bc-1b1c-4b33-b6d4-e59dc24fa00f
DSpace Identifier
http://hdl.handle.net/10263/7583
Recommended Citation
Mukkara, Jagannath, "2.5D Dual-Encoder U-Net for Lesion Segmentation in Chest CT Scans" (2025). Master’s Dissertations. 429.
https://digitalcommons.isical.ac.in/masters-dissertations/429