Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
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 im...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/5/2516 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850034821944836096 |
|---|---|
| author | Niloofar Tavakolian Azadeh Nazemi Ching Yee Suen |
| author_facet | Niloofar Tavakolian Azadeh Nazemi Ching Yee Suen |
| author_sort | Niloofar Tavakolian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4da35daf7bc34f7a8845bf21bceff3ec |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4da35daf7bc34f7a8845bf21bceff3ec2025-08-20T02:57:41ZengMDPI AGApplied Sciences2076-34172025-02-01155251610.3390/app15052516Review of the AI-Based Analysis of Abdominal Organs from Routine CT ScansNiloofar Tavakolian0Azadeh Nazemi1Ching Yee Suen2Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC H3H 2L9, CanadaSchool of Computing, Engineering & The Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UKGina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC H3H 2L9, CanadaAccurate 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.https://www.mdpi.com/2076-3417/15/5/2516medical image segmentationdeep learningconvolutional neural networks (CNN)abdominal trauma analysis |
| spellingShingle | Niloofar Tavakolian Azadeh Nazemi Ching Yee Suen Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans Applied Sciences medical image segmentation deep learning convolutional neural networks (CNN) abdominal trauma analysis |
| title | Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans |
| title_full | Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans |
| title_fullStr | Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans |
| title_full_unstemmed | Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans |
| title_short | Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans |
| title_sort | review of the ai based analysis of abdominal organs from routine ct scans |
| topic | medical image segmentation deep learning convolutional neural networks (CNN) abdominal trauma analysis |
| url | https://www.mdpi.com/2076-3417/15/5/2516 |
| work_keys_str_mv | AT niloofartavakolian reviewoftheaibasedanalysisofabdominalorgansfromroutinectscans AT azadehnazemi reviewoftheaibasedanalysisofabdominalorgansfromroutinectscans AT chingyeesuen reviewoftheaibasedanalysisofabdominalorgansfromroutinectscans |