Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks Article Swipe
Patrick Ferdinand Christ
,
Florian Ettlinger
,
Felix Grün
,
Mohamed Ezzeldin A. Elshaera
,
Jana Lipková
,
Sebastian J. Schlecht
,
Freba Ahmaddy
,
Sunil Tatavarty
,
Marc Bickel
,
Patrick Bilic
,
Markus Rempfler
,
Felix Hofmann
,
Melvin D Anastasi
,
Seyed‐Ahmad Ahmadi
,
Georgios Kaissis
,
Julian Walter Holch
,
Wieland H. Sommer
,
Rickmer Braren
,
Volker Heinemann
,
Bjoern Menze
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1702.05970
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1702.05970
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1702.05970
- https://arxiv.org/pdf/1702.05970
- OA Status
- green
- Cited By
- 184
- References
- 30
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2592329056
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2592329056Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1702.05970Digital Object Identifier
- Title
-
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-02-20Full publication date if available
- Authors
-
Patrick Ferdinand Christ, Florian Ettlinger, Felix Grün, Mohamed Ezzeldin A. Elshaera, Jana Lipková, Sebastian J. Schlecht, Freba Ahmaddy, Sunil Tatavarty, Marc Bickel, Patrick Bilic, Markus Rempfler, Felix Hofmann, Melvin D Anastasi, Seyed‐Ahmad Ahmadi, Georgios Kaissis, Julian Walter Holch, Wieland H. Sommer, Rickmer Braren, Volker Heinemann, Bjoern MenzeList of authors in order
- Landing page
-
https://arxiv.org/abs/1702.05970Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1702.05970Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1702.05970Direct OA link when available
- Concepts
-
Convolutional neural network, Segmentation, Computer science, Artificial intelligence, Robustness (evolution), Pattern recognition (psychology), Liver tumor, Computation, Radiology, Image segmentation, Computer vision, Medicine, Algorithm, Cancer research, Gene, Chemistry, Hepatocellular carcinoma, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
184Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 2, 2023: 7, 2022: 15, 2021: 33Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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