Segmentation-Free Kidney Localization and Volume Estimation Using Aggregated Orthogonal Decision CNNs

  • Mohammad Arafat Hussain
  • Alborz Amir-Khalili
  • Ghassan Hamarneh
  • Rafeef Abugharbieh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

Kidney volume is an important bio-marker in the clinical diagnosis of various renal diseases. For example, it plays an essential role in follow-up evaluation of kidney transplants. Most existing methods for volume estimation rely on kidney segmentation as a prerequisite step, which has various limitations such as initialization-sensitivity and computationally-expensive optimization. In this paper, we propose a hybrid localization-volume estimation deep learning approach capable of (i) localizing kidneys in abdominal CT images, and (ii) estimating renal volume without requiring segmentation. Our approach involves multiple levels of self-learning of image representation using convolutional neural layers, which we show better capture the rich and complex variability in kidney data, demonstrably outperforming hand-crafted feature representations. We validate our method on clinical data of 100 patients with a total of 200 kidney samples (left and right). Our results demonstrate a 55% increase in kidney boundary localization accuracy, and a 30% increase in volume estimation accuracy compared to recent state-of-the-art methods deploying regression-forest-based learning for the same tasks.

Notes

Acknowledgement

We thank Dr. Timothy W. O’Connell and Dr. Mohammed F. Mohammed at VGH for providing the data and ground truth kidney tracing.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohammad Arafat Hussain
    • 1
  • Alborz Amir-Khalili
    • 1
  • Ghassan Hamarneh
    • 2
  • Rafeef Abugharbieh
    • 1
  1. 1.BiSICLUniversity of British ColumbiaVancouverCanada
  2. 2.Medical Image Analysis LabSimon Fraser UniversityBurnabyCanada

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