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Related Experiment Video

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Use of 3D Robotic Ultrasound for In Vivo Analysis of Mouse Kidneys

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Artificial intelligence-based segmentation of small renal masses: a multi-center, multi-scanner, multi-sequence

Mengqiu Cui¹, Zilong Zeng², Silu Chen³

¹Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.

Abdominal Radiology (New York)

|October 31, 2025

Summary

An AI-based automated segmentation method shows promise for detecting and segmenting small renal masses (SRMs) across diverse MRI data. This AI tool achieved high accuracy and good generalization, suggesting its utility in future diagnostic pipelines.

Keywords:

Deep learning Multi-center Multi-sequence MRI Segmentation Small renal mass

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Small renal masses (SRMs) require accurate detection and segmentation for effective diagnosis and treatment planning.
Current segmentation methods can be time-consuming and operator-dependent.
Standardization across different MRI scanners and centers remains a challenge.

Purpose of the Study:

To develop and evaluate an artificial intelligence (AI)-based automated segmentation method for SRMs.
To assess the performance of the AI method using multi-center, multi-scanner, and multi-sequence MRI data.
To determine the generalization ability of the AI method to unseen data and different scanner types.

Main Methods:

Retrospective analysis of 988 pathologically confirmed SRM patient MR images from three centers.
Development of deep learning-based segmentation networks for each MRI sequence.
Evaluation using internal, external, and generalization test sets, assessing detection rate and Dice Similarity Coefficient (DSC).

Main Results:

The AI method achieved high detection rates for SRMs across all patients in the GE test set.
Median DSC ranged from 0.769-0.855 across five MRI sequences for GE scanners.
Reasonable generalization to non-GE scanners was observed, with median DSC ranging from 0.523-0.785.

Conclusions:

Automated AI-based segmentation demonstrates encouraging results in detecting and segmenting SRMs.
The method shows potential for accurate and consistent performance across diverse patient cohorts, scanners, and centers.
This AI tool could become a valuable component in future diagnostic workflows for SRMs.