Drechsler, Klaus ; Knaub, Anton ; Oyarzun Laura, Cristina ; Wesarg, Stefan (2014)
Liver Segmentation in Contrast Enhanced MR Datasets Using a Probabilistic Active Shape and Appearance Model.
IEEE 27th International Symposium on Computer-Based Medical Systems.
doi: 10.1109/CBMS.2014.120
Conference or Workshop Item, Bibliographie
Abstract
The current standard for diagnosing liver tumors is contrast-enhanced multiphase computed tomography. On this basis, several software tools have been developed by different research groups worldwide to support physicians for example in measuring remnant liver volume, analyzing tumors, and planning resections. Several algorithms have been developed to perform these tasks. Most of the time, the segmentation of the liver is at the beginning of the processing chain. Therefore, a vast amount of CT-based liver segmentation algorithms have been developed. However, clinics slowly move from CT as the current gold standard for diagnosing liver diseases towards magnetic resonance imaging. In this work, we utilize a Probabilistic Active Shape Model with an MR specific preprocessing and appearance model to segment the liver in contrast enhanced MR images. Evaluation is based on 8 clinical datasets.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2014 |
| Creators: | Drechsler, Klaus ; Knaub, Anton ; Oyarzun Laura, Cristina ; Wesarg, Stefan |
| Type of entry: | Bibliographie |
| Title: | Liver Segmentation in Contrast Enhanced MR Datasets Using a Probabilistic Active Shape and Appearance Model |
| Language: | English |
| Date: | 2014 |
| Publisher: | IEEE Computer Society Conference Publishing Services (CPS), Los Alamitos, Calif. |
| Event Title: | IEEE 27th International Symposium on Computer-Based Medical Systems |
| DOI: | 10.1109/CBMS.2014.120 |
| Abstract: | The current standard for diagnosing liver tumors is contrast-enhanced multiphase computed tomography. On this basis, several software tools have been developed by different research groups worldwide to support physicians for example in measuring remnant liver volume, analyzing tumors, and planning resections. Several algorithms have been developed to perform these tasks. Most of the time, the segmentation of the liver is at the beginning of the processing chain. Therefore, a vast amount of CT-based liver segmentation algorithms have been developed. However, clinics slowly move from CT as the current gold standard for diagnosing liver diseases towards magnetic resonance imaging. In this work, we utilize a Probabilistic Active Shape Model with an MR specific preprocessing and appearance model to segment the liver in contrast enhanced MR images. Evaluation is based on 8 clinical datasets. |
| Uncontrolled Keywords: | Business Field: Visual decision support, Research Area: Modeling (MOD), Image segmentation, Liver, Tumors, Computed tomography (CT), Magnetic resonance imaging (MRI), Medical imaging, Medical image processing, Medical modeling, Medical diagnosis |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Date Deposited: | 12 Nov 2018 11:16 |
| Last Modified: | 12 Nov 2018 11:16 |
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