AI-Based 3D Segmentation and Volumetric Analysis of Liver Tumors from CT Scans

The Accurate segmentation and volumetric analysis of liver tumors from computed tomography (CT) scans are essential for early diagnosis, treatment planning, and longitudinal assessment of hepatic malignancies. Manual tumor delineation is not only time-consuming but also susceptible to inter- and intra-observer variability, which can affect clinical consistency and treatment outcomes. Automated and robust segmentation methods are therefore highly desirable in modern clinical workflows. This paper proposes an AI-based framework for automated three-dimensional (3D) liver tumor segmentation and volumetric analysis using deep learning. The framework is designed to process full CT volumes and to accurately identify tumor regions despite variations in tumor size, shape, and contrast. By leveraging volumetric learning, the proposed approach preserves spatial continuity across slices, leading to improved segmentation performance compared to conventional two-dimensional methods. Comprehensive preprocessing steps, including intensity normalization, resampling, and noise reduction, are applied to standardize CT data and enhance model generalization. A 3D convolutional neural network architecture is employed to extract multi-scale features that capture both fine-grained tumor boundaries and broader anatomical context. Post-processing strategies such as morphological operations and connected component analysis are utilized to refine segmentation outputs and reduce false detections. Based on the generated 3D segmentation masks, volumetric analysis is conducted to compute tumor volume and related quantitative metrics. These measurements provide objective indicators for tumor burden evaluation, disease progression monitoring, and treatment response assessment, enabling more informed clinical decision-making and personalized therapy planning.