Abstract:
Segmenting vertebral bodies (VBs) and intervertebral discs (IVDs) in magnetic resonance imaging (MRI) data is an important step towards the creation of 3D spine models fo...Show MoreMetadata
Abstract:
Segmenting vertebral bodies (VBs) and intervertebral discs (IVDs) in magnetic resonance imaging (MRI) data is an important step towards the creation of 3D spine models for image-guided surgical treatement of adolescent idiopathic scoliosis (AIS). Recent advances in deep learning have established state-of-the-art results in medical image segmentation. Thus, in this paper, we present a method based on convolutional neural networks to simultaneously segment VBs and IVDs in MRI data sets of AIS patients, a difficult problem which has not yet been adressed in the literature. Our architecure is inspired by the U-net architecture, combined with the recently proposed and promising squeeze-and-excitation (SE) block and an objective function incorporating Cohen's kappa to deal with the imbalamce class problem. Our model is first trained using a public dataset of non-AIS patients, then fine tuned using a few images of AIS patients. Results in 8 test AIS patient MRI volumes show that the fine tuning and SE strategies both improve segmentation considerably while remaining highly complementary to each other.
Date of Conference: 08-11 April 2019
Date Added to IEEE Xplore: 11 July 2019
ISBN Information: