Dual-Stage AI Model for Enhanced CT Imaging: Precision Segmentation of Kidney and Tumors.

ABSTRACT:

Objectives

Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully automated AI model using vision transformers (ViTs) and convolutional neural networks (CNNs) to detect and segment kidneys and kidney tumors in Contrast-Enhanced (CECT) scans, with a focus on improving sensitivity for small, indistinct tumors.

Methods

The segmentation framework employs a ViT-based model for the kidney organ, followed by a 3D UNet model with enhanced connections and attention mechanisms for tumor detection and segmentation. Two CECT datasets were used: a public dataset (KiTS23: 489 scans) and a private institutional

SUBMITTER: Karunanayake N 

PROVIDER: S-EPMC11769543 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature