期刊
2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019)
卷 -, 期 -, 页码 480-484出版社
IEEE
DOI: 10.1109/isbi.2019.8759413
关键词
Deep learning; CNN; segmentation; scoliosis; MRI; vertbral body; intervertebral disc; attention model
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 imbalance 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.
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