Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
Source abstract
Abstract
Accurate and timely segmentation of liver trauma in computed tomography (CT) images is essential for effective diagnosis and management in emergency medicine. This review examines advancements in liver segmentation techniques from 1993 to 2024, focusing on deep learning models and their impact on improving diagnostic accuracy for liver injuries. Early methods relied on basic image processing, which faced limitations due to noise, intensity variations, and complex abdominal anatomy. The advent of deep learning has transformed this domain, with architectures such as UNet, UNet++, UNet3+, multiscale large kernel (MSLUNet), and Swin-Unet achieving significant improvements in segmentation precision. Additionally, generative adversarial networks (GANs), including conditional GAN and pixel-to-pixel (Pix2Pix) GAN, have further enhanced image quality and detail, addressing deficiencies in traditional methods. This review provides a comparative analysis of these models, highlighting their strengths and limitations in liver injury segmentation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Radiomics and Machine Learning in Medical Imaging · Artificial Intelligence in Healthcare and Education · Advanced X-ray and CT Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC H3H 2L9, Canada
School of Computing, Engineering & The Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
References (44)
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