Abstract

The objective of this work is to automatically generate unified reports of lumbar spinal MRIs in the field of radiology, i.e., given an MRI of a lumbar spine, directly generate a radiologist-level report to support clinical decision making. We show that this can be achieved via a weakly supervised framework that combines deep learning and symbolic program synthesis theory to overcome four inevitable tasks: semantic segmentation, radiological classification, positional labeling, and structural captioning. The weakly supervised framework using object level annotations without requiring radiologist-level report annotations to generate unified reports. Each generated report covers almost type lumbar structures comprised of six intervertebral discs, six neural foramina, and five lumbar vertebrae. The contents of each report contain the exact locations and pathological correlations of these lumbar structures as well as their normalities in terms of three type relevant spinal diseases: intervertebral disc degeneration, neural foraminal stenosis, and lumbar vertebrae deformities. This framework is applied to a large corpus of T1/T2-weighted sagittal MRIs of 253 subjects acquired from multiple vendors. Extensive experiments demonstrate that the framework is able to generate unified radiological reports, which reveals its effectiveness and potential as a clinical tool to relieve spinal radiologists from laborious workloads to a certain extent, such that contributes to relevant time savings and expedites the initiation of many specific therapies.

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