Segmentation of small animal PET/CT mouse brain scans using an MRI-based 3D digital atlas

被引：2
作者：
Delzescaux, Thierry
Lebenberg, Jessica
Raguet, Hugo
Hantraye, Philippe
机构：
来源：
2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2010年
关键词：
IMAGES;
D O I：
10.1109/IEMBS.2010.5626106
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
The work reported in this paper aimed at developing and testing an automated method to calculate the biodistribution of a specific PET tracer in mouse brain PET/CT images using an MRI-based 3D digital atlas. Surface-based registration strategy and affine transformation estimation were considered. Such an approach allowed overcoming the lack of anatomical information in the inner regions of PET/CT brain scans. Promising results were obtained in one mouse (on two scans) and will be extended to a neuroinflammation mouse model to characterize the pathology and its evolution. Major improvements are expected regarding automation, time computation, robustness and reproducibility of mouse brain segmentation. Due to its generic implementation, this method could be successfully applied to PET/CT brain scans of other species (rat, primate) for which 3D digital atlases are available.
引用
收藏
页码：3097 / 3100
页数：4
相关论文
共 50 条
[31]   MRI-Based Attenuation Correction for Hybrid PET/MRI Systems: A 4-Class Tissue Segmentation Technique Using a Combined Ultrashort-Echo-Time/Dixon MRI Sequence [J].
Berker, Yannick ;
Franke, Jochen ;
Salomon, Andre ;
Palmowski, Moritz ;
Donker, Henk C. W. ;
Temur, Yavuz ;
Mottaghy, Felix M. ;
Kuhl, Christiane ;
Izquierdo-Garcia, David ;
Fayad, Zahi A. ;
Kiessling, Fabian ;
Schulz, Volkmar .
JOURNAL OF NUCLEAR MEDICINE, 2012, 53 (05) :796-804
[32]   Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach [J].
Kirisli, Hortense A. ;
Schaap, Michiel ;
Klein, Stefan ;
Neefjes, Lisan A. ;
Weustink, Annick C. ;
van Walsum, Theo ;
Niessen, Wiro J. .
MEDICAL IMAGING 2010: IMAGE PROCESSING, 2010, 7623
[33]   Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface [J].
Ahmadvand, Payam ;
Duggan, Noirin ;
Benard, Francois ;
Hamarneh, Ghassan .
MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2016, 2016, 10019 :271-278
[34]   Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study [J].
Gong, Xiaoliang ;
Ma, Chao ;
Yang, Panpan ;
Chen, Yufei ;
Du, Chaolin ;
Fu, Caixia ;
Lu, Jian-Ping .
ACTA RADIOLOGICA OPEN, 2019, 8 (03)
[35]   Iterative machine learning based rotational alignment of brain 3D CT data [J].
Chmelik, Jiri ;
Jakubicek, Roman ;
Vicar, Tomas ;
Walek, Petr ;
Ourednicek, Petr ;
Jan, Jiri .
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2019, :4404-4408
[36]   A novel hybrid atlas-free hierarchical graph-based segmentation of newborn brain MRI using wavelet filter banks [J].
Jaware, Tushar ;
Khanchandani, Kamlesh ;
Badgujar, Ravindra .
INTERNATIONAL JOURNAL OF NEUROSCIENCE, 2020, 130 (05) :499-514
[37]   Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets [J].
Kotowski, Krzysztof ;
Adamski, Szymon ;
Malara, Wojciech ;
Machura, Bartosz ;
Zarudzki, Lukasz ;
Nalepa, Jakub .
BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES (BRAINLES 2020), PT II, 2021, 12659 :265-277
[38]   An automatic cascaded approach for pancreas segmentation via an unsupervised localization using 3D CT volumes [J].
Jain, Suchi ;
Sikka, Geeta ;
Dhir, Renu .
MULTIMEDIA SYSTEMS, 2023, 29 (04) :2337-2349
[39]   3D Supervoxel based features for early detection of AD: A microscopic view to the brain MRI [J].
Mishra, Shiwangi ;
Beheshti, Iman ;
Tanveer, M. ;
Khanna, Pritee .
MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (16) :22481-22496
[40]   ROI-Based Compression Strategy of 3D MRI Brain Datasets for Wireless Communications [J].
Dhouib, D. ;
Nait-Ali, A. ;
Olivier, C. ;
Naceur, M. S. .
IRBM, 2021, 42 (03) :146-153
12345