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FIST: Fast Interactive Segmentation of Tumors

Steger, Sebastian and Sakas, Georgios (2012):
FIST: Fast Interactive Segmentation of Tumors.
In: Lecture Notes in Computer Science (LNCS); 7029, Springer, Berlin, Heidelberg, New York, In: Abdominal Imaging: Computational and Clinical Applications, pp. 125-132, DOI: 10.1007/978-3-642-28557-8₁₆,
[Conference or Workshop Item]

Abstract

Automatic segmentation methods for tumors are typically only suitable for a specific type of tumor in a specific imaging modality and sometimes lack in accuracy whereas manual tumor segmentation achieves the desired results but is very time consuming. Interactive segmentation however speeds up the process while still being able to maintain the accuracy of manual segmentation. This paper presents a novel method for fast interactive segmentation of tumors (called FIST) from medical images, which is suitable for all somewhat spherical tumors in any 3d medical imaging modality. The user clicks in the center of the tumor and a belief propagation based iterative adaption process is initiated, thereby considering image gradients as well as local smoothness priors of the surface. During that process, instant visual feedback is given, enabling to intervene in the adaption process by sketching parts of the contour in any cross section. The approach has successfully been applied to the segmentation of liver tumors in CT datasets. Satisfactory results could be achieved in 15.21 seconds on the average. Further trials on oropharynx tumors, liver tumors and the prostate from MR images as well as lymph nodes and the bladder from CT volumes demonstrate the generality of the presented approach.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Steger, Sebastian and Sakas, Georgios
Title: FIST: Fast Interactive Segmentation of Tumors
Language: English
Abstract:

Automatic segmentation methods for tumors are typically only suitable for a specific type of tumor in a specific imaging modality and sometimes lack in accuracy whereas manual tumor segmentation achieves the desired results but is very time consuming. Interactive segmentation however speeds up the process while still being able to maintain the accuracy of manual segmentation. This paper presents a novel method for fast interactive segmentation of tumors (called FIST) from medical images, which is suitable for all somewhat spherical tumors in any 3d medical imaging modality. The user clicks in the center of the tumor and a belief propagation based iterative adaption process is initiated, thereby considering image gradients as well as local smoothness priors of the surface. During that process, instant visual feedback is given, enabling to intervene in the adaption process by sketching parts of the contour in any cross section. The approach has successfully been applied to the segmentation of liver tumors in CT datasets. Satisfactory results could be achieved in 15.21 seconds on the average. Further trials on oropharynx tumors, liver tumors and the prostate from MR images as well as lymph nodes and the bladder from CT volumes demonstrate the generality of the presented approach.

Series Name: Lecture Notes in Computer Science (LNCS); 7029
Publisher: Springer, Berlin, Heidelberg, New York
Uncontrolled Keywords: Business Field: Digital society, Research Area: Confluence of graphics and vision, Interactive segmentation, Tumors
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Abdominal Imaging: Computational and Clinical Applications
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/978-3-642-28557-8₁₆
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