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

By P. Christ, Florian Ettlinger, Felix Grün, Mohamed Ezzeldin A. Elshaer, Jana Lipková, Sebastian J. Schlecht, F. Ahmaddy, S. Tatavarty, Marc Bickel, Patrick Bilic and 10 more. arXiv.org, 2017

365 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b8e9fc311a6f2d9e60a62d8e18163b208cbe0b3c

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Citations in the corpus

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges (2019, methodology): “A popular solution for this issue is sample re-weighting, where a higher weight is applied to the foreground patches during training [16].”
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II (2017, neutral): “One method [2] distorts data by projecting it onto a sphere (unit norm).”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “Example architectures that were successfully used in both nat-ural images and radiology applications are encoder-decoder architectures such as U-Net [64, 65, 66] or Fully Convolutional DenseNet [67, 68, 69].”
A Review of Deep-Learning-Based Medical Image Segmentation Methods (2021, background): “[84] proposed cascaded fully convolutional neural networks (CFCNs) to automatically segment liver and lesions in CT or MRI abdomen images.”
RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans (2018, methodology): “Additionally, Christ et al. (2017b) also predicted hepatocellular carcinoma (HCC) malignancy using two CNN architectures.”