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Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

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arxiv 1702.05970 v2 pith:5LTKJBPB submitted 2017-02-20 cs.CV cs.AI

Patrick Ferdinand Christ , Florian Ettlinger , Felix Grün , Mohamed Ezzeldin A. Elshaera , Jana Lipkova , Sebastian Schlecht , Freba Ahmaddy , Sunil Tatavarty
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Marc Bickel Patrick Bilic Markus Rempfler Felix Hofmann Melvin D Anastasi Seyed-Ahmad Ahmadi Georgios Kaissis Julian Holch Wieland Sommer Rickmer Braren Volker Heinemann Bjoern Menze
This is my paper · ORCID
classification cs.CVcs.AI
keywords liversegmentationlesionssteptumorvolumesautomaticcascaded
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Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of a large-scale medical trial or quantitative image analysis. We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions within the predicted liver ROIs of step 1. CFCN models were trained on an abdominal CT dataset comprising 100 hepatic tumor volumes. Validations on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume. We further experimentally demonstrate the robustness of the proposed method on an 38 MRI liver tumor volumes and the public 3DIRCAD dataset.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation

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    IPA-CP, an iterative pseudo-labeling and uncertainty-adaptive copy-paste scheme, is claimed to improve semi-supervised tumor segmentation in CT scans over state-of-the-art baselines.

  2. ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths

    cs.CV 2019-08 conditional novelty 4.0 of 10

    ACE-Net, a U-net variant with augmented contracting and expansive blocks, achieves competitive results on EM neuron and retinal vessel segmentation.

  3. Automatic segmentation of kidney and liver tumors in CT images

    eess.IV 2019-08 conditional novelty 4.0 of 10

    A LinkNet-34 model with transfer learning, augmentation, and a BCE-plus-Dice loss reaches DICE 78.8% for liver tumors on 3DIRCADb and 67.4% tumor DICE on KiTS-2019.

  4. Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity

    cs.CV 2019-08 conditional novelty 3.0 of 10

    Automated knee osteoarthritis grading with jointly trained classification-regression CNNs reaches 64.6% accuracy and ordinal regression 64.3%, outperforming WNDCHRM but not the cited Siamese CNN baseline.

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