What question did this study set out to answer?

This research aims to automate slice selection and improve localization in lumbar spine MRI for better diagnosis of intervertebral disc disorders.

January 6, 2026

SpineDeep ‐Net: Dual‐Self‐Attention‐Based Deep Neural Network for Automating Slice Selection and Precise Transverse Plane Localization in Lumbar Spine MRI for Intervertebral Disc Analysis

Key Points

This research aims to automate slice selection and improve localization in lumbar spine MRI for better diagnosis of intervertebral disc disorders.
Developed a dual-self-attention-based deep neural network framework, SpineDeep-Net.
Focused on automatic selection of transverse planes for lumbar spine MRI disc slices.
Evaluated performance metrics including accuracy and specificity.
Achieved 96.83% accuracy and 98.41% specificity in experiments.
Outperformed existing state-of-the-art methods in diagnosing lumbar spine conditions.

Abstract

ABSTRACT The rising prevalence of lumbar spine disorders demands scalable solutions for mass screening and automated diagnosis. Accurate analysis of specific MRI slices, such as mid‐sagittal or transverse mid‐height intervertebral disc (IVD) slices, is essential but currently relies on time‐consuming, error‐prone manual selection. Automating this process is crucial to enhance the efficiency and accuracy of computer‐aided diagnostic systems. To address this need, this study introduces a novel deep learning‐based framework—SpineDeep‐Net that integrates self‐attention mechanisms within a multi‐layer convolutional neural network for automatic selection of optimal transverse planes of lumbar spine MRI disc slices. By focusing on mid‐height slices of L3/L4, L4/L5, and L5/S1 IVDs—the most diagnostically relevant slices, SpineDeep‐Net eliminates the reliance on manual selection processes, thereby accelerating and improving the diagnostic pipeline. Unlike standard attention, the proposed dual‐self‐attention employs two sequential attention stages that jointly enhance long‐range spatial cue extraction and emphasize subtle disc‐level differences. This mechanism enables the model to focus more effectively on diagnostically relevant regions within lumbar MRI slices by dynamically recalibrating feature maps and strengthening feature dependencies. Experimental evaluations demonstrate the superior performance of SpineDeep‐Net, achieving 96.83% accuracy and 98.41% specificity, outperforming state‐of‐the‐art methods. By automating the selection and classification of clinically critical disc slices, SpineDeep‐Net addresses a key challenge in lumbar spine diagnostics, providing a reliable, scalable, and efficient tool that aids radiologists in making informed clinical decisions. The proposed framework highlights the transformative potential of self‐attention‐guided deep learning in advancing healthcare diagnostics. The source code is publicly available at https://github.com/rakeshchandrajoshi/spinedeepnet .

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