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Liver disease classification from ultrasound using multi-scale CNN

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Ultrasound (US) is the preferred modality for fatty liver disease diagnosis due to its noninvasive, real-time, and cost-effective imaging capabilities. However, traditional B-mode US is qualitative, and therefore, the assessment is very subjective. Computer-aided diagnostic tools can improve the specificity and sensitivity of US and help clinicians to perform uniform diagnoses.

Methods

In this work, we propose a novel deep learning model for nonalcoholic fatty liver disease classification from US data. We design a multi-feature guided multi-scale residual convolutional neural network (CNN) architecture to capture features of different receptive fields. B-mode US images are combined with their corresponding local phase filtered images and radial symmetry transformed images as multi-feature inputs for the network. Various fusion strategies are studied to improve prediction accuracy. We evaluate the designed network architectures on B-mode in vivo liver US images collected from 55 subjects. We also provide quantitative results by comparing our proposed multi-feature CNN architecture against traditional CNN designs and machine learning methods.

Results

Quantitative results show an average classification accuracy above 90% over tenfold cross-validation. Our proposed method achieves a 97.8% area under the ROC curve (AUC) for the patient-specific leave-one-out cross-validation (LOOCV) evaluation. Comprehensive validation results further demonstrate that our proposed approaches achieve significant improvements compared to training mono-feature CNN architectures (\(p<0.05\)).

Conclusions

Feature combination is valuable for the traditional classification methods, and the use of multi-scale CNN can improve liver classification accuracy. Based on the promising performance, the proposed method has the potential in practical applications to help radiologists diagnose nonalcoholic fatty liver disease.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA

    Hui Che & Ilker Hacihaliloglu

  2. Department of Surgery, Rutgers New Jersey Medical School, New Brunswick, NJ, USA

    Lloyd G. Brown

  3. Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

    David J. Foran

  4. Department of Radiology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA

    John L. Nosher & Ilker Hacihaliloglu

Authors
  1. Hui Che
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  2. Lloyd G. Brown
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  3. David J. Foran
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  4. John L. Nosher
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  5. Ilker Hacihaliloglu
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Correspondence to Ilker Hacihaliloglu.

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Che, H., Brown, L.G., Foran, D.J. et al. Liver disease classification from ultrasound using multi-scale CNN. Int J CARS 16, 1537–1548 (2021). https://doi.org/10.1007/s11548-021-02414-0

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  • DOI: https://doi.org/10.1007/s11548-021-02414-0

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