Integrated Multi-label 3D Deep Learning Multi-task Model for Intelligent MR Spine Scan Planning

Motivation: Obtaining consistent spine MRI images irrespective of patient posture, spine deformities, and technologists’ skills, with minimal disruption in the existing workflow. Goal(s): To develop an intelligent scan plane prescription for spine MRI using deep learning on regular 3-plane localizer images. Approach: We adopted a multi-resolution CNN network for multiple segmentation tasks - spine vertebrae, intervertebral disc (IVD), and saturation band (SB) across all the spine stations (cervical, thoracic, and lumbar) and orientations (sagittal and coronal). Results: We reported good segmentation of vertebrae and IVD, along with consistent SB placement with angle error of less than 5 degree and no overlap with the spine region. Impact: We present a first-of-its-kind integrated multi-label 3D DL model that operates on 2D 3-plane regular localizers to aid consistent MRI scan planning. This model combines MRI localizer images across orientation, across spine stations, and across multiple imaging tasks.