Multi-task neural networks for joint hippocampus segmentation and clinical score regression

Abstract

Feature representations extracted from hippocampus in magnetic resonance (MR) images are widely used in computer-aided Alzheimer’s disease (AD) diagnosis, and thus accurate segmentation for the hippocampus has been remaining an active research topic. Previous studies for hippocampus segmentation require either human annotation which is tedious and error-prone or pre-processing MR images via time-consuming non-linear registration. Although many automatic segmentation approaches have been proposed, their performance is often limited by the small size of hippocampus and complex confounding information around the hippocampus. In particular, human-engineered features extracted from segmented hippocampus regions (e.g., the volume of the hippocampus) are essential for brain disease diagnosis, while these features are independent of diagnosis models, leading to sub-optimal performance. To address these issues, we propose a multi-task deep learning (MDL) method for joint hippocampus segmentation and clinical score regression using MR images. The prominent advantages of our MDL method lie on that we don’t need any time-consuming non-linear registration for pre-processing MR images, and features generated by MDL are consistent with subsequent diagnosis models. Specifically, we first align all MR images onto a standard template, followed by a patch extraction process to approximately locate hippocampus regions in the template space. Using image patches as input data, we develop a multi-task convolutional neural network (CNN) for joint hippocampus segmentation and clinical score regression. The proposed CNN network contains two subnetworks, including 1) a U-Net with a Dice-like loss function for hippocampus segmentation, and 2) a convolutional neural network with a mean squared loss function for clinical regression. Note that these two subnetworks share a part of network parameters, to exploit the inherent association between these two tasks. We evaluate the proposed method on 407 subjects with MRI data from baseline Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results suggest that our MDL method achieves promising results in both tasks of hippocampus segmentation and clinical score regression, compared with several state-of-the-art methods.

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Notes

  1. 1.

    http://adni.loni.usc.edu/data-samples/mri/

  2. 2.

    http://mipav.cit.nih.gov/index.php

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Acknowledgments

This study was supported by National Natural Science Foundation of China (Nos. 61703301, 61573023) one more support grant and University Science and Technology Project of Shandong Province (No. J17KA086).

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Correspondence to Jun Zhang.

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Cao, L., Li, L., Zheng, J. et al. Multi-task neural networks for joint hippocampus segmentation and clinical score regression. Multimed Tools Appl 77, 29669–29686 (2018). https://doi.org/10.1007/s11042-017-5581-1

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Keywords

  • Hippocampus segmentation
  • Clinical score regression
  • Brain disease diagnosis
  • Convolutional neural network