3D MRI heart segmentation of mouse embryos

被引:10
作者
Zouagui, T. [1 ]
Chereul, E. [2 ]
Janier, M. [2 ]
Odet, C. [3 ]
机构
[1] Univ Sci & Technol Oran, Dept Elect, Image & Signal Lab, Oran 31000, Algeria
[2] Animage, F-69677 Bron, France
[3] Creatis Insa Lyon, F-69621 Villeurbanne, France
关键词
3D image segmentation; Deformable models; Triangular meshes; Mesh refinement; Deformable pyramid; Cardiac imaging; MRI; Mouse embryo; Heart defects; CARDIAC MR; IMAGES; TRACKING; MODELS;
D O I
10.1016/j.compbiomed.2009.11.001
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
MRI has become an effective tool for anatomical mice studies. Currently, embryologists study the development of mouse embryos in order to understand the mechanisms of human development. The aim of the research presented in this paper, is to develop a semi-automatic image segmentation framework based 3D deformable models to identify cardiac malformations which are a major cause of death in children. The segmentation systems have been used to segment 3D mouse embryos heart structures. Results on the ventricles and on the heart muscle are presented and compared with manually segmented models. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:64 / 74
页数:11
相关论文
共 50 条
[31]   CAN3D: FAST 3D KNEE MRI SEGMENTATION VIA COMPACT CONTEXT AGGREGATION [J].
Dai, Wei ;
Woo, Boyeong ;
Liu, Siyu ;
Marques, Matthew ;
Tang, Fangfang ;
Crozier, Stuart ;
Engstrom, Craig ;
Chandra, Shekhar .
2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 2021, :1505-1508
[32]   3D level set method for blastomere segmentation of preimplantation embryos in fluorescence microscopy images [J].
Grushnikov, Andrey ;
Niwayama, Ritsuya ;
Kanade, Takeo ;
Yagi, Yasushi .
MACHINE VISION AND APPLICATIONS, 2018, 29 (01) :125-134
[33]   CPS-based fully automatic cardiac left ventricle and left atrium segmentation in 3D MRI [J].
Ahmad, Ibtihaj ;
Hussain, Farhan ;
Khan, Shoab Ahmad ;
Akram, Usman ;
Jeon, Gwanggil .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2019, 36 (05) :4153-4164
[34]   3D MRI of the Knee [J].
Altahawi, Faysal ;
Pierce, Jason ;
Aslan, Mercan ;
Li, Xiaojuan ;
Winalski, Carl S. ;
Subhas, Naveen .
SEMINARS IN MUSCULOSKELETAL RADIOLOGY, 2021, 25 (03) :455-467
[35]   Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation [J].
Toan Duc Bui ;
Shin, Jitae ;
Moon, Taesup .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2019, 54
[36]   Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI [J].
Lee, Junghoon ;
Woo, Jonghye ;
Xing, Fangxu ;
Murano, Emi Z. ;
Stone, Maureen ;
Prince, Jerry L. .
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2014, 38 (08) :714-724
[37]   3D PBV-Net: An automated prostate MRI data segmentation method [J].
Jin, Yao ;
Yang, Guang ;
Fang, Ying ;
Li, Ruipeng ;
Xu, Xiaomei ;
Liu, Yongkai ;
Lai, Xiaobo .
COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 128
[38]   Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm [J].
Rosalia Maglietta ;
Nicola Amoroso ;
Marina Boccardi ;
Stefania Bruno ;
Andrea Chincarini ;
Giovanni B. Frisoni ;
Paolo Inglese ;
Alberto Redolfi ;
Sabina Tangaro ;
Andrea Tateo ;
Roberto Bellotti .
Pattern Analysis and Applications, 2016, 19 :579-591
[39]   Deep Learning Based Ensemble Approach for 3D MRI Brain Tumor Segmentation [J].
Tien-Bach-Thanh Do ;
Dang-Linh Trinh ;
Minh-Trieu Tran ;
Lee, Guee-Sang ;
Kim, Soo-Hyung ;
Yang, Hyung-Jeong .
BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES, BRAINLES 2021, PT II, 2022, 12963 :210-221
[40]   Skull Segmentation in 3D Neonatal MRI using Hybrid Hopfield Neural Network [J].
Daliri, M. ;
Moghaddam, H. Abrishami ;
Ghadimi, S. ;
Momeni, M. ;
Harirchi, F. ;
Giti, M. .
2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2010, :4060-4063