HEPATIC VESSEL SEGMENTATION AND CLASSIFICATION IN CTA IMAGES USING NNU-NET WITH CENTERLINE REGRESSION

Abstract

Manual segmentation of liver vessels is a challenging and time-consuming task for radiologists, prone to variability and misclassification, which can affect treatment planning for conditions like hepatocellular carcinoma. To address this, the study utilized the nnU-Net framework as a baseline and introduced several enhancements, participating in the VEELA 2025 challenge. The VEELA 2025 challenge comprises 20 train and 20 test hepatic and portal vein labeled computed tomography angiography (CTA) images for segmentation and classification. The methodology involved training on a combination of CTA and contrast-enhanced CT images from datasets including the Medical Segmentation Decathlon, LIRCAD, and the VEELA 2025 challenge data, due to the limited availability of labeled CTA images. Five different models were tested, primarily nnU-Net based, each with a different loss function incorporating elements like Dice Similarity Coefficient (DSC), cross-entropy loss, centerline Dice loss (clDice) to promote topological continuity, and edge map Dice loss to improve boundary precision. The final model, which employed a weighted loss strategy prioritizing pixel-wise classification while reinforcing spatial consistency and structure preservation, achieved the top ranking in the VEELA 2025 challenge. Key contributions include using centerline regression cross-entropy loss for minor vessel classification, weighted losses for balanced optimization, and leveraging contrast-enhanced CT data to overcome CTA data scarcity.