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Abstract #5040

DESS vs T1-FLASH 3D-MRI for Knee Cartilage Segmentation: An Evaluation Using Deep 3D-CNN

Archit Raj1 and Harsh Kumar Agarwal1

1Health and Medical Equipment, Samsung R&D Institute India Bangalore Pvt. Ltd., Bangalore, India

Automated knee cartilage segmentation can potentially improve the clinical utility of the MRI assessment of knee osteoarthritis due to the convoluted structure of the knee cartilage in 3D. Recently deep convolutional neural network (CNN) have shown better performance for knee cartilage segmentation. Unlike other segmentation algorithms deep-CNN techniques learn the model parameters from the data itself. Therefore, this abstract proposed that deep 3D-CNN techniques can be used to determine the optimal MRI sequence for knee cartilage segmentation and demonstrated that 3D-DESS MRI have statistically better segmentation performance as compared to 3D-T1-FLASH MRI.

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