Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning

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Abstract

Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automated pathogenesis-based diagnosis would simultaneously localize and grade multiple spinal organs (neural foramina, vertebrae, intervertebral discs) to diagnose LNFS and discover pathogenic factors. The automated way facilitates planning optimal therapeutic schedules and relieving clinicians from laborious workloads. However, no successful work has been achieved yet due to its extreme challenges since 1) multiple targets: each lumbar spine has at least 17 target organs, 2) multiple scales: each type of target organ has structural complexity and various scales across subjects, and 3) multiple tasks, i.e., simultaneous localization and diagnosis of all lumbar organs, are extremely difficult than individual tasks. To address these huge challenges, we propose a deep multiscale multitask learning network (DMML-Net) integrating a multiscale multi-output learning and a multitask regression learning into a fully convolutional network. 1) DMML-Net merges semantic representations to reinforce the salience of numerous target organs. 2) DMML-Net extends multiscale convolutional layers as multiple output layers to boost the scale-invariance for various organs. 3) DMML-Net joins a multitask regression module and a multitask loss module to prompt the mutual benefit between tasks. Extensive experimental results demonstrate that DMML-Net achieves high performance (0.845 mean average precision) on T1/T2-weighted MRI scans from 200 subjects. This endows our method an efficient tool for clinical LNFS diagnosis.

Keywords

Neural foraminal stenosis Multiscale learning Multitask learning Deep learning 

Notes

Acknowledgements

This work was made possible through support from Natural Science Foundation of Shandong Province in China (ZR2015FM010), Project of Shandong Province Higher Educational Science and Technology Program in China (No. J15LN20), Project of Shandong Province Traditional Chinese Medicine Technology Development Program in China (2015-026, 2017-001), and Project of Shandong Province Medical and Health Technology Development Program in China (No. 2016WS0577).

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. 1.College of Science and TechnologyShandong University of Traditional Chinese MedicineJinanChina
  2. 2.Computational Medicine LabShandong University of Traditional Chinese MedicineJinanChina
  3. 3.Department of Medical ImagingWestern UniversityLondonCanada
  4. 4.Digital Image Group (DIG)LondonCanada

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