Search research articles

Search research articles

Related Experiment Video

Updated: Jul 12, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

SLIC-Former: a superpixel-guided transformer framework for automatic liver segmentation in CT images.

Sarah F Elqersh^1,2, Amira Y Haikal³, Mahmoud M Saafan³

¹Department of Computers and Systems, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt. sarahelqersh174@std.mans.edu.eg.

Scientific Reports

|July 10, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

DMARS_WGO: a deep reinforcement-driven hybrid metaheuristic for intelligent adaptive optimization.

Scientific reports·2026

Same author

Dynamic quality aware path planning for 6 DoF robotic arms using BiRRT and metaheuristic optimization based on B spline paths.

Scientific reports·2026

Same author

Cross-dataset late fusion of Camera-LiDAR and radar models for object detection.

Scientific reports·2026

Same author

Advancements in accident-aware traffic management: a comprehensive review of V2X-based route optimization.

Scientific reports·2025

Same author

Intelligent fault tolerance control using long short-term memory for efficient system performance under fault conditions.

Scientific reports·2025

Same author

Optimizing the Steering of Driverless Personal Mobility Pods with a Novel Differential Harris Hawks Optimization Algorithm (DHHO) and Encoder Modeling.

Sensors (Basel, Switzerland)·2024

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study introduces SLIC-Former, a novel transformer framework for precise liver segmentation in computed tomography (CT) scans. The method enhances accuracy and efficiency by using superpixels, improving anatomical boundary adherence.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate liver segmentation in CT scans is challenging due to irregular organ shape and similar adjacent tissue appearance.
Existing methods struggle with efficiency and precision in abdominal CT image analysis.

Purpose of the Study:

To develop an accurate and efficient automatic liver segmentation framework for abdominal CT scans.
To introduce SLIC-Former, a superpixel-guided transformer model for improved segmentation.

Main Methods:

Proposed SLIC-Former, a framework utilizing Simple Linear Iterative Clustering (SLIC) for superpixel generation.
Replaced fixed image patches with adaptive superpixels to align with anatomical boundaries and reduce computation.
Evaluated on the Liver Tumor Segmentation (LiTS) dataset.

Keywords:

Computed Tomography (CT)Deep learning Liver segmentation Medical image analysis Superpixels (SLIC)Vision transformers

More Related Videos

Hybrid µCT-FMT imaging and image analysis

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jul 12, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Hybrid µCT-FMT imaging and image analysis

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Main Results:

Achieved a Dice coefficient of 0.93, an Intersection over Union (IoU) of 0.87, and a Volumetric Overlap Error (VOE) of 13.5%.
Demonstrated high overlap with expert annotations and produced smooth, coherent liver masks.
SLIC-Former proved computationally efficient compared to traditional patch-based methods.

Conclusions:

SLIC-Former provides an accurate and efficient tool for automatic liver segmentation in CT images.
The superpixel-guided approach offers a promising foundation for segmenting other organs and enhancing clinical decision support systems.