Multi-resolution deep network ensembles for cervical intervertebral disc segmentation are biased by trainer

Ien Li; Kirstin Cook; Michael Le; Bilwaj K. Gaonkar; Luke Macyszyn M.D.

doi:10.1117/12.2580990

15 February 2021 Multi-resolution deep network ensembles for cervical intervertebral disc segmentation are biased by trainer

Ien Li, Kirstin Cook, Michael Le, Bilwaj K. Gaonkar, Luke Macyszyn M.D.

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 1159715 (2021) https://doi.org/10.1117/12.2580990
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

The diagnosis of cervical spinal canal stenosis currently relies on radiological reports, based largely on subjective interpretation of magnetic resonance images (MRIs). This leads to a high degree of variation in diagnosis and treatment of pathology in the cervical spine. Despite the capabilities of MRI to provide high contrast and high spatial resolution 3D images, the prevalence of inconsistent diagnosis has presented a challenge for delivering necessary care. Recently, deep learning (DL) based segmentation algorithms have demonstrated great potential for automating the measurement and segmentation of cervical spine anatomy. We show the results of a Deep-U-Net ensemble based automated pipeline to detect and segment intervertebral discs at the C1-C7 level in sagittal spine MRIs. Training was done using scans from (n = 50) individuals wherein discs were segmented by a trained human rater (R1). Validation was performed on (n = 50) scans by comparing automated segmentations to those performed by two human raters (R1 and R2). We show that the average Dice similarity coefficient was 0.79 when comparing algorithm to R1, 0.76 when comparing algorithm to R2, and 0.78 when comparing R1 to R2. Further, segmentations by R1, R2 and algorithm differed (ANOVA with p ≤.0002), and the median Dice score comparing algorithm to R1 was greater than that comparing algorithm to R2, indicating bias toward R1. Thus, our preliminary demonstration highlights the potential of using deep learning techniques to create reliable, quantitative biomarkers to diagnose and measure cervical spine pathology. Our work additionally cautions us to the challenges posed by rater-specific bias induced by the training process in deep learning segmentation algorithms.

Conference Presentation

Citation Download Citation

Ien Li, Kirstin Cook, Michael Le, Bilwaj K. Gaonkar, and Luke Macyszyn M.D. "Multi-resolution deep network ensembles for cervical intervertebral disc segmentation are biased by trainer", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 1159715 (15 February 2021); https://doi.org/10.1117/12.2580990

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