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Updated: Jun 16, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Deep Learning Framework for Liver Segmentation from T₁-Weighted MRI Images.

Md Sakib Abrar Hossain^1,2, Sidra Gul^3,4, Muhammad E H Chowdhury²

¹NSU Genome Research Institute (NGRI), North South University, Dhaka 1229, Bangladesh.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

This summary is machine-generated.

This study developed a novel cascaded network for liver segmentation in MRI scans, achieving high accuracy. This machine learning approach aids in computer-aided diagnosis for liver conditions.

Keywords:

MRI T1-weighted contrast automated liver segmentation deep learning diagnostic radiology

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Published on: July 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Liver segmentation in radiological images is challenging due to anatomical variability.
Magnetic Resonance Imaging (MRI) offers superior soft tissue contrast for liver pathology diagnosis compared to CT scans.
Automatic segmentation of liver from MRI is difficult due to the absence of Hounsfield unit-based preprocessing.

Purpose of the Study:

To investigate state-of-the-art segmentation networks for liver segmentation in volumetric MRI.
To develop and evaluate a novel cascaded network for accurate liver segmentation from MRI slices.

Main Methods:

Utilized T1-weighted (in-phase) MRI scans from the CHAOS dataset (20 patients, 647 slices).
Evaluated twenty-four diverse state-of-the-art segmentation networks with various encoder/decoder backbones.
Proposed and implemented a novel cascaded network architecture for axial liver slice segmentation.

Main Results:

The proposed cascaded network achieved a Dice Similarity Coefficient (DSC) of 95.15%.
The network obtained an Intersection over Union (IoU) score of 92.10%.
The developed framework demonstrated superior performance compared to existing methods on the same test set.

Conclusions:

The novel cascaded network provides highly accurate liver segmentation from MRI.
This automated approach can significantly enhance computer-aided diagnosis for liver diseases.
The method offers a robust solution for challenging MRI-based liver segmentation tasks.