Interactive level set segmentation for image-guided therapy

Image-guided therapy procedures require the patient to remain still throughout the image acquisition, data analysis and therapy. This imposes a tight time constraint on the over-all process. Automatic extraction of the pathological regions prior to the therapy can be faster than the customary manual...

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Main Authors: Kiryati, Nahum, Ben-Zadok, Nir, Riklin-Raviv, Tammy
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:en_US
Published: Institute of Electrical and Electronics Engineers 2010
Subjects:
Online Access:http://hdl.handle.net/1721.1/55357
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author Kiryati, Nahum
Ben-Zadok, Nir
Riklin-Raviv, Tammy
author2 Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
author_facet Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Kiryati, Nahum
Ben-Zadok, Nir
Riklin-Raviv, Tammy
author_sort Kiryati, Nahum
collection MIT
description Image-guided therapy procedures require the patient to remain still throughout the image acquisition, data analysis and therapy. This imposes a tight time constraint on the over-all process. Automatic extraction of the pathological regions prior to the therapy can be faster than the customary manual segmentation performed by the physician. However, the image data alone is usually not sufficient for reliable and unambiguous computerized segmentation. Thus, the oversight of an experienced physician remains mandatory. We present a novel segmentation framework, that allows user feedback. A few mouse-clicks of the user, discrete in nature, are represented as a continuous energy term that is incorporated into a level-set functional. We demonstrate the proposed method on MR scans of uterine fibroids acquired prior to focused ultrasound ablation treatment. The experiments show that with a minimal user input, automatic segmentation results become practically identical to manual expert segmentation.
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spelling mit-1721.1/553572022-09-30T23:45:52Z Interactive level set segmentation for image-guided therapy Kiryati, Nahum Ben-Zadok, Nir Riklin-Raviv, Tammy Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Riklin-Raviv, Tammy Riklin-Raviv, Tammy User interaction MR scans segmentation Level-set framework Image guided therapy Image-guided therapy procedures require the patient to remain still throughout the image acquisition, data analysis and therapy. This imposes a tight time constraint on the over-all process. Automatic extraction of the pathological regions prior to the therapy can be faster than the customary manual segmentation performed by the physician. However, the image data alone is usually not sufficient for reliable and unambiguous computerized segmentation. Thus, the oversight of an experienced physician remains mandatory. We present a novel segmentation framework, that allows user feedback. A few mouse-clicks of the user, discrete in nature, are represented as a continuous energy term that is incorporated into a level-set functional. We demonstrate the proposed method on MR scans of uterine fibroids acquired prior to focused ultrasound ablation treatment. The experiments show that with a minimal user input, automatic segmentation results become practically identical to manual expert segmentation. A.M.N. Foundation for the Advancement of Science, Art and Culture in Israel 2010-05-28T20:36:48Z 2010-05-28T20:36:48Z 2009-08 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-3932-4 1945-7928 INSPEC Accession Number: 10814218 http://hdl.handle.net/1721.1/55357 Ben-Zadok, N., T. Riklin-Raviv, and N. Kiryati. “Interactive level set segmentation for image-guided therapy.” Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on. 2009. 1079-1082. ©2009 Institute of Electrical and Electronics Engineers. en_US http://dx.doi.org/10.1109/ISBI.2009.5193243 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009. ISBI '09 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE
spellingShingle User interaction
MR scans segmentation
Level-set framework
Image guided therapy
Kiryati, Nahum
Ben-Zadok, Nir
Riklin-Raviv, Tammy
Interactive level set segmentation for image-guided therapy
title Interactive level set segmentation for image-guided therapy
title_full Interactive level set segmentation for image-guided therapy
title_fullStr Interactive level set segmentation for image-guided therapy
title_full_unstemmed Interactive level set segmentation for image-guided therapy
title_short Interactive level set segmentation for image-guided therapy
title_sort interactive level set segmentation for image guided therapy
topic User interaction
MR scans segmentation
Level-set framework
Image guided therapy
url http://hdl.handle.net/1721.1/55357
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