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

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A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI.

Nuno M Rodrigues^1,2, Sara Silva¹, Leonardo Vanneschi³

¹LASIGE, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal.

|March 11, 2023

Summary

This study compared prostate cancer segmentation models, finding nnU-Net superior. Pre-processing with object detection improved generalization for prostate imaging analysis.

Keywords:

deep learning prostate cancer prostate detection prostate segmentation

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

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Published on: January 7, 2019

Area of Science:

Medical Imaging
Oncology
Artificial Intelligence

Background:

Prostate cancer is a leading global cancer, necessitating advanced clinical aid systems for early detection and treatment.
Current segmentation models for prostate gland and zones require comparative evaluation for improved clinical utility.

Purpose of the Study:

To comparatively analyze common segmentation models for prostate gland and zones.
To evaluate the efficacy of object detection as a pre-processing step for prostate segmentation.

Main Methods:

Retrospective study utilizing two public datasets for cross-validation and external testing.
Comparative evaluation of deep learning segmentation models.
Assessment of object detection pre-processing impact on segmentation performance.

Main Results:

Most segmentation models performed similarly, with nnU-Net demonstrating consistent outperformance.
Models trained on object-detector-cropped data showed improved generalization.
Object detection pre-processing led to better external test set performance despite lower cross-validation scores.

Conclusions:

Model selection has minimal impact on segmentation accuracy, except for nnU-Net's superior performance.
Object detection pre-processing enhances the generalization capabilities of prostate segmentation models.
Further research into object detection integration can optimize clinical aid systems for prostate cancer.