A Lightweight Convolutional Neural Network Based on Dynamic Level‐Set Loss Function for Spine MR Image Segmentation

Abstract

BackgroundSpine MR image segmentation is important foundation for computer‐aided diagnostic (CAD) algorithms of spine disorders. Convolutional neural networks segment effectively, but require high computational costs.PurposeTo design a lightweight model based on dynamic level‐set loss function for high segmentation performance.Study TypeRetrospective.PopulationFour hundred forty‐eight subjects (3163 images) from two separate datasets. Dataset‐1: 276 subjects/994 images (53.26% female, mean age 49.02 ± 14.09), all for disc degeneration screening, 188 had disc degeneration, 67 had herniated disc. Dataset‐2: public dataset with 172 subjects/2169 images, 142 patients with vertebral degeneration, 163 patients with disc degeneration.Field Strength/SequenceT2 weighted turbo spin echo sequences at 3T.AssessmentDynamic Level‐set Net (DLS‐Net) was compared with four mainstream (including U‐net++) and four lightweight models, and manual label made by five radiologists (vertebrae, discs, spinal fluid) used as segmentation evaluation standard. Five‐fold cross‐validation are used for all experiments. Based on segmentation, a CAD algorithm of lumbar disc was designed for assessing DLS‐Net's practicality, and the text annotation (normal, bulging, or herniated) from medical history data were used as evaluation standard.Statistical TestsAll segmentation models were evaluated with DSC, accuracy, precision, and AUC. The pixel numbers of segmented results were compared with manual label using paired t‐tests, with P < 0.05 indicating significance. The CAD algorithm was evaluated with accuracy of lumbar disc diagnosis.ResultsWith only 1.48% parameters of U‐net++, DLS‐Net achieved similar accuracy in both datasets (Dataset‐1: DSC 0.88 vs. 0.89, AUC 0.94 vs. 0.94; Dataset‐2: DSC 0.86 vs. 0.86, AUC 0.93 vs. 0.93). The segmentation results of DLS‐Net showed no significant differences with manual labels in pixel numbers for discs (Dataset‐1: 1603.30 vs. 1588.77, P = 0.22; Dataset‐2: 863.61 vs. 886.4, P = 0.14) and vertebrae (Dataset‐1: 3984.28 vs. 3961.94, P = 0.38; Dataset‐2: 4806.91 vs. 4732.85, P = 0.21). Based on DLS‐Net's segmentation results, the CAD algorithm achieved higher accuracy than using non‐cropped MR images (87.47% vs. 61.82%).Data ConclusionThe proposed DLS‐Net has fewer parameters but achieves similar accuracy to U‐net++, helps CAD algorithm achieve higher accuracy, which facilitates wider application.Evidence Level2Technical EfficacyStage 1