The project is aiming to phenotype early- to mid-gestational mouse embryos by segmenting select organ systems in 3D data sets acquired in-utero with high-frequency ultrasound (HFU). Around 20,000 NIH Knockout (KO) mouse strains will be generated, 30% of which are expected to be embryonic or perinatal lethal, including many important models of human structural birth defects and congenital diseases. The development of phenotyping methods for embryonic lethal mice that provide for efficient pipeline analyses of defects in embryonic growth in the KO mouse strains is a highly demanded.
We propose to develop and validate in-utero 3D HFU image acquisition protocols and image processing methods that permit noninvasive, longitudinal studies of embryonic development and, in particular, the characterization of mutant phenotypes. Volumetric HFU data will be collected in-utero from mouse embryos staged between E9.5 to 15.5 in order to establish a database of normal development. With provided HFU images of mouse embryos, the focus of our team is developing advanced image analysis and machine learning methods for analyzing brain development in mouse embryos and characterizing defects caused by mutations.
Some Initial Result:
Figure 1 Segmentation of Brain Ventricles
Figure 2 Next Step: Organ Segmentation
National Institute of Health
Jeffrey Ketterling, Dr. Lizzi Center for Biomedical Engineering, Riverside Research, New York
Daniel Turnbull, Professor Skirball Institute of Biomolecular Medicine, NYU School of Medicine
Yao Wang1, Professor NYU Video Lab, NYU Tandon School of Engineering
Jen-wei Kuo1, NYU Video Lab, NYU Tandon School of Engineering
Orlando Aristizabal2, Skirball Institute of Biomolecular Medicine, NYU School of Medicine
Ziming Qiu1, NYU Video Lab, NYU Tandon School of Engineering
Jack Langerman1, Computer Science,NYU Tandon School of Engineering
- Jen-wei Kuo, Yao Wang, Orlando Aristizabal, Jeffrey A. Ketterling, Jonathan Mamou"Automatic Mouse Embryo Brain Ventricle Segmentation from 3D 40-MHz Ultrasound Data", IEEE International Ultrasonics Symposium (IUS), July 2013.
- Jen-wei Kuo, Jonathan Mamou, Yao Wang, Emi Saegusa-Beecroft, Junji Machi, Ernest J. Feleppa "A Novel Nested Graph Cuts Method for Segmenting Human Lymph Nodes in 3D High Frequency Ultrasound Images", 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 372-375. IEEE, 2015.
- Jen-wei Kuo, Jonathan Mamou, Orlando Aristizábal, Xuan Zhao, Jeffrey A. Ketterling, Yao Wang "Nested Graph Cut for Automatic Segmentation of High-Frequency Ultrasound Images of the Mouse Embryo", IEEE transactions on medical imaging 35, no. 2 (2016): 427-441.
- Jen-wei Kuo, Jonathan Mamou, Yao Wang, Emi Saegusa-Beecroft, Junji Machi, and Ernest J. Feleppa, Segmentation of 3D High-frequency Ultrasound Images of Human Lymph Nodes Using Graph Cut with Energy Functional Adapted to Local Intensity Distribution, Transactions on Ultrasonics Ferroelectrics and Frequency Control (TUFFC), 2017.
- Jen-wei Kuo, Ziming Qiu, Orlando Aristizbal, Jonathan Mamou, Daniel H. Turnbull, Jeffrey Ketterling, and Yao Wang, Automatic Body Localization and Brain Ventricle Segmentation in 3D High Frequency Ultrasound Images of Mouse Embryos, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, 2018, pp. 635-639.