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Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling

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Deep Learning and Convolutional Neural Networks for Medical Image Computing

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Accurate automatic detection and segmentation of abdominal organs from CT images is important for quantitative and qualitative organ tissue analysis, detection of pathologies, surgical assistance as well as computer-aided diagnosis (CAD). In general, the large variability of organ locations, the spatial interaction between organs that appear similar in medical scans and orientation and size variations are among the major challenges of organ segmentation. The pancreas poses these challenges in addition to its flexibility which allows for the shape of the tissue to vastly change. In this chapter, we present a fully automated bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans. The method is a four-stage system based on a hierarchical cascade of information propagation by classifying image patches at different resolutions and cascading (segments) superpixels . System components consist of the following: (1) decomposing CT slice images as a set of disjoint boundary-preserving superpixels; (2) computing pancreas class probability maps via dense patch labeling; (3) classifying superpixels by pooling both intensity and probability features to form empirical statistics in cascaded random forest frameworks; and (4) simple connectivity based post-processing. Evaluation of the approach is conducted on a database of 80 manually segmented CT volumes in sixfold cross validation. Our achieved results are comparable, or better to the state-of-the-art methods (evaluated by “leave-one-patient-out”), with a Dice coefficient of \(70.7\%\) and Jaccard Index of \(57.9\%\). The computational efficiency of the proposed approach is drastically improved in the order of 6–8 min, compared to other methods of \({\ge }10\) hours per testing case.

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Notes

  1. 1.

    http://www.ics.uci.edu/~ihler/code/kde.html.

  2. 2.

    The axial reconstruction CT scans in our study have largely varying ranges or extends in the z-axis. If some anatomical landmarks, such as the bottom plane of liver, the center of kidneys, can be provided automatically, the anatomically normalized z-coordinate positions for superpixels can be computed and used as an additional spatial feature for RF classification.

  3. 3.

    https://code.google.com/p/cuda-convnet2.

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Authors and Affiliations

  1. Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20837, USA

    Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey & Ronald M. Summers

Authors
  1. Amal Farag
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  2. Le Lu
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  3. Holger R. Roth
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  4. Jiamin Liu
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  5. Evrim Turkbey
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  6. Ronald M. Summers
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Corresponding author

Correspondence to Le Lu .

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Editors and Affiliations

  1. NIH Clinical Center, Bethesda, Maryland, USA

    Le Lu

  2. Siemens Healthcare Technology Center, Princeton, New Jersey, USA

    Yefeng Zheng

  3. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  4. University of Florida, Gainesville, Florida, USA

    Lin Yang

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Farag, A., Lu, L., Roth, H.R., Liu, J., Turkbey, E., Summers, R.M. (2017). Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling. In: Lu, L., Zheng, Y., Carneiro, G., Yang, L. (eds) Deep Learning and Convolutional Neural Networks for Medical Image Computing. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-42999-1_16

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  • DOI: https://doi.org/10.1007/978-3-319-42999-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42998-4

  • Online ISBN: 978-3-319-42999-1

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