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A Weak and Semi-supervised Segmentation Method for Prostate Cancer in TRUS Images.

Seokmin Han¹, Sung Il Hwang², Hak Jong Lee^3,4

¹Department of Computer Science and Information Engineering, Korea National University of Transportation, Uiwang-si, Kyunggi-do, South Korea.

Journal of Digital Imaging

|February 12, 2020

Summary

This summary is machine-generated.

This study introduces a weak and semi-supervised deep learning method for prostate cancer segmentation in ultrasound images, reducing radiologist workload. The approach effectively utilizes incomplete annotations, achieving competitive results with less fully annotated data.

Keywords:

Deep learning Prostate cancer Segmentation TRUS Weak and semi-supervision

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Prostate cancer segmentation in ultrasound images is crucial for diagnosis and treatment planning.
Manual segmentation by radiologists is time-consuming and requires extensive expertise.
Deep learning models typically require large, fully annotated datasets, which are often unavailable.

Purpose of the Study:

To develop and evaluate a weak and semi-supervised deep learning framework for automated prostate cancer segmentation.
To reduce the reliance on fully annotated data for training segmentation models.
To alleviate the burden on radiologists by automating the segmentation process.

Main Methods:

A novel weak and semi-supervised deep learning framework was proposed.
The framework utilized a combination of strongly (pixel-wise) and weakly (lesion location) supervised data.
Iterative retraining and label refinement were employed to improve segmentation accuracy using limited annotations.

Main Results:

The proposed method achieved a mean intersection over union (mIoU) of approximately 0.6 with 40% strong supervision.
Performance was comparable to, though slightly lower than, fully supervised methods (approx. 2% decrease).
The framework demonstrated effectiveness in training neural networks with incomplete annotations.

Conclusions:

Weak and semi-supervised learning offers a viable approach for prostate cancer segmentation in ultrasound images.
The developed framework can significantly reduce the need for extensive manual annotation.
This method holds promise for improving efficiency and accessibility in radiological workflows.