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Fast interactive medical image segmentation with weakly supervised deep learning method.

Kibrom Berihu Girum^1,2, Gilles Créhange^3,4, Raabid Hussain³

¹ImViA Laboratory, University of Burgundy, Dijon, France. kibrom-berihu_girum@etu.u-bourgogne.fr.

International Journal of Computer Assisted Radiology and Surgery

|July 13, 2020

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This summary is machine-generated.

This study introduces a fast, weakly supervised deep learning method for accurate medical image segmentation, overcoming data limitations and speeding up annotation processes for improved clinical applications.

Area of Science:

Medical Imaging
Deep Learning
Computer-Aided Diagnosis

Background:

Accurate medical image segmentation is crucial for analysis and interventions.
Deep learning requires large, diverse annotated datasets, which are often limited due to expert knowledge and laborious manual labeling.
Developing fast, interactive, and weakly supervised methods is highly desirable.

Purpose of the Study:

To develop an efficient deep learning method for accurate medical image segmentation.
To generate annotated datasets for deep learning using a novel approach.
To address the limitations of data availability and annotation in medical imaging.

Main Methods:

A generative neural network predicts prior knowledge (contour proposal) from pseudo-contour landmarks.

Keywords:

Brachytherapy CNN Domain adaptation Echocardiography Generative model Weakly supervised segmentation

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A convolutional neural network refines the contour proposal using predicted knowledge and raw image data.

The method was evaluated on prostate brachytherapy (TRUS/CT) and echocardiographic images.

Main Results:

The model achieved high segmentation accuracy on prostate images (Dice: 96.9±0.9%, 95.4±0.9%) and echocardiograms (Dice: 96.3±1.3%).
Segmentation was performed rapidly, with processing times as low as 7.79 milliseconds per image.
Quantitative metrics included Dice coefficient, 3D Hausdorff distance, and volumetric overlap ratio.

Conclusions:

A fast, interactive, and accurate deep learning method for medical image segmentation was developed and validated.
The approach can mitigate challenges related to inter-clinical variations in medical imaging data.
The method shows potential to accelerate image annotation processes for deep learning applications.