Deep neural network for automated simultaneous intervertebral disc (IVDs) identification and segmentation of multi-modal MR images

Das, Pabitra and Pal, Chandrajit and Acharyya, Amit and Chakrabarti, Amlan and Basu, Saumyajit (2021) Deep neural network for automated simultaneous intervertebral disc (IVDs) identification and segmentation of multi-modal MR images. Computer Methods and Programs in Biomedicine, 205 (106074). ISSN 01692607

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Abstract

Background and objective: Lower back pain in humans has become a major risk. Classical approaches follow a non-invasive imaging technique for the assessment of spinal intervertebral disc (IVDs) abnormalities, where identification and segmentation of discs are done separately, making it a time-consuming phenomenon. This necessitates designing a robust automated and simultaneous IVDs identification and segmentation of multi-modality MRI images. Methods: We introduced a novel deep neural network architecture coined as ‘RIMNet’, a Region-to-Image Matching Network model, capable of performing an automated and simultaneous IVDs identification and segmentation of MRI images. The multi-modal input data is being fed to the network with a dropout strategy, by randomly disabling modalities in mini-batches. The performance accuracy as a function of the testing dataset was determined. The execution of the deep neural network model was evaluated by computing the IVDs Identification Accuracy, Dice coefficient, MDOC, Average Symmetric Surface Distance, Jaccard Coefficient, Hausdorff Distance and F1 Score. Results:Proposed model has attained 94% identification accuracy, dice coefficient value of 91.7±1% in segmentation and MDOC 90.2±1%. Our model also achieved 0.87±0.02 for Jaccard Coefficient, 0.54±0.04 for ASD and 0.62±0.02 mm Hausdorff Distance. The results have been validated and compared with other methodologies on dataset of MICCAI IVD 2018 challenge. Conclusions: Our proposed deep-learning methodology is capable of performing simultaneous identification and segmentation on IVDs MRI images of the human spine with high accuracy.

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IITH Creators:

IITH Creators	ORCiD
Acharyya, Amit	http://orcid.org/0000-0002-5636-0676
Pal, C.	UNSPECIFIED

Item Type:

Article

Uncontrolled Keywords:

Automation; Convolutional neural networks; Geometry; Identification (control systems); Image matching; Image segmentation; Magnetic resonance imaging; Network architecture; Statistical tests; Convolutional neural network; Deep learning; Dice coefficient; Identification; Identification accuracy; Intervertebral disk; MRI Image; Multi-modal; Region-to-image matching; Segmentation

Subjects:

Electrical Engineering

Divisions:

Department of Electrical Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

21 Jun 2021 11:58

Last Modified:

21 Jun 2021 11:58

URI:

http://raiithold.iith.ac.in/id/eprint/7967