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Registration of Free-Breathing 3D+t Abdominal Perfusion CT Images via Co-segmentation

Lucidarme, Olivier; Rouet, Laurence; Mory, Benoît; Cuingnet, Rémi; Romain, Blandine; Prevost, Raphaël (2013), Registration of Free-Breathing 3D+t Abdominal Perfusion CT Images via Co-segmentation, in Navab, Nassir; Barillot, Christian; Sato, Yoshinobu; Sakuma, Ichiro; Mori, Kensaku, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, Springer, p. 99-107. http://dx.doi.org/10.1007/978-3-642-40763-5_13

Type
Communication / Conférence
Date
2013
Conference title
MICCAI 2013
Conference date
2013-08
Conference city
Nagoya
Conference country
Japon
Book title
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Book author
Navab, Nassir; Barillot, Christian; Sato, Yoshinobu; Sakuma, Ichiro; Mori, Kensaku
Publisher
Springer
Series title
Lecture Notes in Computer Science
Series number
8150
ISBN
Print ISBN 978-3-642-40762-8 Online ISBN 978-3-642-40763-5
Pages
99-107
Publication identifier
http://dx.doi.org/10.1007/978-3-642-40763-5_13
Metadata
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Author(s)
Lucidarme, Olivier cc
Rouet, Laurence
Mory, Benoît
Cuingnet, Rémi
Romain, Blandine
Prevost, Raphaël
Abstract (EN)
Dynamic contrast-enhanced computed tomography (DCE-CT) is a valuable imaging modality to assess tissues properties, particularly in tumours, by estimating pharmacokinetic parameters from the evolution of pixels intensities in 3D+t acquisitions. However, this requires a registration of the whole sequence of volumes, which is challenging especially when the patient breathes freely. In this paper, we propose a generic, fast and automatic method to address this problem. As standard iconic registration methods are not robust to contrast intake, we rather rely on the segmentation of the organ of interest. This segmentation is performed jointly with the registration of the sequence within a novel co-segmentation framework. Our approach is based on implicit template deformation, that we extend to a co-segmentation algorithm which provides as outputs both a segmentation of the organ of interest in every image and stabilising transformations for the whole sequence. The proposed method is validated on 15 datasets acquired from patients with renal lesions and shows improvement in terms of registration and estimation of pharmacokinetic parameters over the state-of-the-art method.
Subjects / Keywords
Health Informatics; Imaging / Radiology; Artificial Intelligence (incl. Robotics); Computer Graphics; Pattern Recognition; Image Processing and Computer Vision

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