Fang-Cheng (Frank) Yeh, MD, PhD

Assistant Professor
Director, Fiber Tractography Lab

Fang-Cheng Yeh




Fang-Cheng (Frank) Yeh, MD, PhD, joined the Department of Neurological Surgery in 2016 as a tenure-track assistant professor.

Prior to joining the faculty at the University of Pittsburgh, Dr. Yeh received his MD degree from National Taiwan University and completed his PhD study in biomedical engineering at Carnegie Mellon University in 2014.

Dr. Yeh is currently working on diffusion MRI and its role as image biomarkers for neurological and psychiatric disorders. His research focuses on novel applications of computational methods to brain connectome research, a challenging field with a lot of known unknowns and unsolved questions that require extensive technological development. He has developed several diffusion MRI methods and applied them to both clinical and translational studies.

Dr. Yeh is known for his development of DSI Studio, an integrated platform for diffusion MRI analysis, fiber tracking, and 3D tractography visualization. In 2018 alone, DSI Studio facilitated more than 100 peer-reviewed publications. DSI Studio provides the core technique for “high accuracy fiber tracking,” which has been widely used by many research groups to investigate how major fiber pathways are affected by neurological and psychiatric diseases. In an open compettition sponsored by the International Society for Magnetic Resonance in Medicine (ISMRM) in 2015, Dr. Yeh’s method achieved the highest valid connection score (92.49%, ID:03) among 96 different approaches submitted by a total of 20 groups from around the world.

Dr. Yeh also developed WS-Recognizer, an open-source quantitative pathology tool that analyzes whole slide image and automatically recognizes targets. WS-Recognizer has been used to correlate pathology finding with MRI and visualize tissue characteristics in a panoramic view across the entire tissue section.

Specialized Areas of Interest

Diffusion MRI, tractography, network analysis, medical image analysis, pathology informatics.

Professional Organization Membership

International Society for Magnetic Resonance in Medicine 

Education & Training

MD, National Taiwan University, 2006
PhD, Biomedical Engineering, Carnegie Mellon University, 2014

Selected Publications

Yeh FC, Vettel JM, Singh A, Poczos B, Grafton ST, Erickson KI, Tseng WI, Verstynen TD. Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome FingerprintsPLoS Comput Biol 12(11):e1005203, 2016.

Yeh FC, Badre D, Verstynen T. Connectometry: A statistical approach harnessing the analytical potential of the local connectome. Neuroimage 125:162-71, 2016.

Fernández-Miranda JC, Wang Y, Pathak S, Stefaneau L, Verstynen T, Yeh FC. Asymmetry, connectivity, and segmentation of the arcuate fascicle in the human brain. Brain Struct Funct 220(3):1665-80, 2014.

Wang Y, Fernández-Miranda JC, Verstynen T, Pathak S, Schneider W, Yeh FC. Rethinking the role of the middle longitudinal fascicle in language and auditory pathways. Cereb Cortex 23(10):2347-56, 2013.

Yeh FC, Verstynen TD, Wang Y, Fernández-Miranda JC, Tseng WY. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS One 8(11):e80713, 2013.

Yeh FC, Tseng WY. NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction. Neuroimage 58(1):91-9, 2011.

Yeh FC, Wedeen VJ, Tseng WY. (2010). Generalized q-sampling imaging. IEEE Trans Med Imaging 29(9):1626-35, 2010.

Yeh FC, Parwani AV, Pantanowitz L, Ho C. Automated grading of renal cell carcinoma using whole slide imaging. Journal of Pathology Informatics 5:23, 2014.

Yeh FC, Ye Q, Hitchens TK, Wu YL, Parwani AV, Ho C. Mapping stain distribution in pathology slides using whole slide imaging. Journal of Pathology Informatics 5:1, 2014.

A complete list of Dr. Yeh's publications can be reviewed through the National Library of Medicine's publication database.

Research Activities

1) A Group Averaged Tractography Atlas

Dr. Yeh introduced an expert-vetted, population-based atlas of the structural connectome derived from diffusion MRI data (N=842). This was achieved by creating a high-resolution template of diffusion patterns averaged across individual subjects and using tractography to generate 550,000 trajectories of representative white matter fascicles. The trajectories were clustered and labeled by a team of experienced neuroanatomists. Multi-level network topology was illustrated by connectograms of the whole brain, subdivisions in the association, projection, and commissural pathways, and individual fiber bundles. This atlas of the structural connectome represents normative neuroanatomical organization of human brain white matter, complimentary to traditional histologically-derived and voxel-based white matter atlases, allowing for better modeling and simulation of brain connectivity for future connectomic studies as well as clinical and educational applications.

2) Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors

Dr. Yeh collaborated with Timothy Verstynen, PhD, associate professor of psychology at Carnegie Mellon University, to study local connectome and its association with neuropsychological factors. The local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel, and it reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here they show how variability in the local connectome is correlated in a principled way across individuals. This intersubject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how the local connectome has both the sensitivity and the specificity to be used as a phenotypic marker for subject-specific attributes.

3) Generalized q-Sampling Imaging Fiber Tractography Reveals Displacement and Infiltration of Fiber Tracts in Low-Grade Gliomas

Dr. Yeh studied 16 patients with a neuropathological diagnosis of LGG (WHO grade II). Peritumoral fiber tracts underwent qualitative and quantitative evaluation. Contralateral hemisphere counterparts were used for comparison. He found that there was a significant increase in S/W ratio among displaced tracts in the pre-operative scans in comparison with the contralateral side. QA values of peritumoral tract segments were significantly lower in infiltrated tracts. WHO grade II LGGs might displace, infiltrate, or cause a combination of displacement and infiltration of WM tracts. QA derived from GQI provides valuable information that helps to differentiate infiltration from displacement. Anisotropy changes correlate with qualitative alterations, which may serve as a potential biomarker of fiber tract integrity.

4) Automatic Target Recognition Using WS Recognizer

Dr. Yeh is collaborating with Liron Pantanowitz, MD, professor of pathology and bio-medical informatics at UPMC Shadyside, to start a new initiative that use whole slide imaging to search for acid fast bacilli in pathology slides. This initiative will develop a tool, WS-Recognizer, which is an open-source pathology tool that uses whole slide image to recognize stains in the slides and present meaningful information.

Media Appearances