Automatic segmentation of the sphenoid sinus in CT-scans volume with DeepMedics 3D CNN architecture

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Automatic segmentation of the sphenoid sinus in CT-scans volume with DeepMedics 3D CNN architecture

Journal Title: UNKNOWN - Year 2019, Vol 3, Issue 1

Abstract

Today, researchers are increasingly using manual, semi-automatic, and automatic segmentation techniques to delimit or extract organs from medical images. Deep learning algorithms are increasingly being used in the area of medical imaging analysis. In comparison to traditional methods, these algorithms are more efficient to obtain compact information, which considerably enhances the quality of medical image analysis system. In this paper, we present a new method to fully automatic segmentation of the sphenoid sinus using a 3D (convolutional neural network). The scarcity of medical data initially forced us through this study to use a 3D CNN model learned on a small data set. To make our method fully automatic, preprocessing and post processing are automated with extraction techniques and mathematical morphologies. The proposed tool is compared to a semi-automatic method and manual deductions performed by a specialist. Preliminary results from CT volumes appear very promising.

Authors and Affiliations

Kamal SOUADIH, Ahror Belaid, Douraied Ben Salem

Keywords

Deep Learning Biomedical Engineering 3D Imaging 3D CNN CT Scan Sphenoid Sinus Automatic Segmentation.

EP ID EP648608
DOI 10.26415/2572-004X-vol3iss1p334-346
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