3D segmentation of mandible from multisectional CT scans by convolutional neural networks

TL;DR

This paper presents a U-Net based convolutional neural network that effectively segments mandibles from multisectional CT scans, achieving high accuracy in 3D surgical planning applications.

Contribution

It introduces a novel multi-plane CNN approach that combines 2D segmentations into accurate 3D mandible models from CT scans.

Findings

Achieved an average dice coefficient of 0.89 on test scans.

Demonstrated high accuracy in mandible segmentation.

Validated approach on 11 CT scans.

Abstract

Segmentation of mandibles in CT scans during virtual surgical planning is crucial for 3D surgical planning in order to obtain a detailed surface representation of the patients bone. Automatic segmentation of mandibles in CT scans is a challenging task due to large variation in their shape and size between individuals. In order to address this challenge we propose a convolutional neural network approach for mandible segmentation in CT scans by considering the continuum of anatomical structures through different planes. The proposed convolutional neural network adopts the architecture of the U-Net and then combines the resulting 2D segmentations from three different planes into a 3D segmentation. We implement such a segmentation approach on 11 neck CT scans and then evaluate the performance. We achieve an average dice coefficient of $ 0.89 $ on two testing mandible segmentation. Experimental results show that our proposed approach for mandible segmentation in CT scans exhibits high accuracy.