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

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Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and

Mostefa Ben Naceur¹, Mohamed Akil², Rachida Saouli³

¹Gaspard Monge Computer Science Laboratory, Univ Gustave Eiffel, CNRS, ESIEE Paris, F-77454 Marne-la-Vallée, France; Smart Computer Sciences Laboratory, Computer Sciences Department, Exact.Sc, and SNL, University of Biskra, Algeria.

Medical Image Analysis

|May 18, 2020

Summary

A new Deep Convolutional Neural Networks (CNNs) model accurately segments brain tumors using selective attention. This AI model achieves state-of-the-art performance on MRI scans, outperforming radiologists.

Keywords:

Brain tumor segmentation Class-imbalance Convolutional neural networks Fully automatic Glioblastomas Overlapping patches

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Glioblastoma multiforme (GBM) segmentation is crucial for treatment planning and outcome prediction.
Accurate segmentation of high- and low-grade GBM tumors from MRI is challenging due to tumor heterogeneity and complex boundaries.
Current segmentation methods often require manual input, are time-consuming, and may lack consistency.

Purpose of the Study:

To develop a fully automatic Deep Convolutional Neural Networks (CNNs) model for accurate segmentation of high- and low-grade Glioblastoma brain tumors.
To enhance feature extraction from MRI images using a selective attention mechanism inspired by the human visual system.
To address challenges of class-imbalance and spatial relationships in image patch analysis for improved segmentation.

Main Methods:

Proposed a novel CNNs model incorporating a selective attention mechanism to maximize relevant feature extraction from MRI scans.
Addressed class-imbalance through equal sampling of image patches and weighted cross-entropy loss.
Investigated the impact of Overlapping Patches versus Adjacent Patches, finding overlapping patches superior for incorporating global and local context.

Main Results:

The proposed End-to-End Deep Learning model achieved state-of-the-art performance on the BRATS-2018 dataset.
Achieved median Dice scores of 0.90 (whole tumor), 0.83 (tumor core), and 0.83 (enhancing tumor), surpassing radiologist performance (74%-85%).
The model demonstrated computational efficiency, segmenting the whole brain in an average of 12 seconds.

Conclusions:

The developed Deep Learning model provides accurate and reliable automatic segmentation of Glioblastoma brain tumors.
The selective attention mechanism and optimized patch handling significantly improve segmentation performance.
The model's efficiency and accuracy make it suitable for research and clinical applications in neuro-oncology.