Medical-image Analysis and Statistical Interpretation (MASI) Lab

Methodology pipeline. High resolution BDA micrographs are registered to the corresponding digital photograph of the frozen tissue block, which is registered to the 3D diffusion MRI volume. From the micrograph, BDA is automatically segmented, resulting in a BDA density map. From diffusion MRI, tractography is performed, resulting in tract density maps. Direct, voxel-by-voxel comparisons can now be made between histology and diffusion tractography.

Anatomical Accuracy of Standard-Practice Tractography Algorithms in the Motor System – a Histological Validation in the Squirrel Monkey Brain

Dec. 15, 2018—Kurt G. Schilling, Yurui Gao, Vaibhav Janve, Iwona Stepniewska, Bennett Landman, Adam Anderson. “Anatomical Accuracy of Standard-Practice Tractography Algorithms in the Motor System – a Histological Validation in the Squirrel Monkey Brain”. Magnetic Resonance Imaging. 2018. doi: 1016/j.mri.2018.09.004 Full text: https://www.ncbi.nlm.nih.gov/pubmed/30213755 Abstract For two decades diffusion fiber tractography has been used to probe both the spatial extent of...

HARFI is able to capture complex, asymmetric functional distributions. Fiber fanning (A, B; red arrows), three‐way crossings (C; blue arrows), and bending (D; black arrows) voxels are reconstructed in regions expected to contain complex distributions

Functional Tractography of White Matter by High Angular Resolution Functional-correlation Imaging (HARFI)

Dec. 15, 2018—Kurt G. Schilling, Yurui Gao, Muwei Li, Tung-Lin Wu, Justin Blaber, Bennett A Landman, Adam W Anderson, Zhaohua Ding, John C Gore. “Functional Tractography of White Matter by High Angular Resolution Functional-correlation Imaging (HARFI)”. Magn Reson Med. 2018;00:1-14. https://doi.org/10.1002/mrm.27512. Full text: https://www.ncbi.nlm.nih.gov/pubmed/30277272 https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.27512 Abstract Purpose Functional magnetic resonance imaging with BOLD contrast is widely used for detecting...

Diffusion tractograms for randomly selected submissions. Tractography is shown in the coronal and sagittal planes, for both macaque pathways (A), the squirrel monkey pathway (B), and all 16 phantom bundles (C).

Limits to anatomical accuracy of diffusion tractography using modern approaches

Dec. 15, 2018—Kurt G Schilling, Vishwesh Nath, Colin Hansen, Prasanna Parvathaneni, Justin Blaber, Yurui Gao, Peter Neher, Dogu Baran Aydogan, Yonggang Shi, Mario Ocampo-Pineda, Simona Schiavi, Alessandro Daducci , Gabriel Girard, Muhamed Barakovic, Jonathan Rafael-Patino, David Romascano, Gaëtan Rensonnet, Marco Pizzolato, Alice Bates, Elda Fischi, Jean-Philippe Thiran, Erick J. Canales-Rodríguez, Chao Huang, Hongtu Zhu, Liming Zhong, Ryan...

Fig. 1. Past challenges in fiber tractography. Detailed description of data, ground truth, and evaluation are described in the text. (A) FiberCup Phantom pathways with 16 ground truth bundles [21]. (B) Eight example CST reconstructions from the DTI Challenge [23]. (C) Synthetic fiber fields from the HARDI Reconstruction Challenge [26]. (D) Phantomas [27] dataset for Tractometer evaluation [28]. (E) Creation of simulated in vivo human dataset for the ISMRM Tractography Challenge [29]. (F) Example submissions from the TraCED Reproducibility Challenge for two white matter pathways. (G) 3D-VoTEM ground truths defined on the macaque, squirrel monkey, and phantom (from left to right).

Challenges in diffusion MRI tractography – Lessons learned from international benchmark competitions

Dec. 15, 2018—Kurt G Schilling, Alessandro Daducci, Klaus Maier-Hein, Cyril Poupon, Jean-Christophe Houde, Vishwesh Nath, Adam W Anderson, Bennett A Landman, Maxime Descoteaux. “Challenges in Diffusion MRI Tractography – Lessons Learned from International Benchmark Competitions”. Magnetic Resonance Imaging. 2018. doi: 10.1016/j.mri.2018.11.014 Full text: NIHMSID https://www.sciencedirect.com/science/article/pii/S0730725X18305162#f0005 Abstract Diffusion MRI (dMRI) fiber tractography has become a pillar of the neuroimaging...

Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images

Dec. 14, 2018—Zhoubing Xu, Yuankai Huo, JinHyeong Park, Bennett Landman, Andy Milkowski, Sasa Grbic, and Shaohua Zhou. “Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images.” In International Conference on Medical Image Computing and Computer-Assisted Intervention. vol 11071. pp. 711—719. Springer, Cham, 2018. Open Access ArXiv Download Abstract An abdominal ultrasound examination, which...

Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data

Dec. 14, 2018—Yuankai Huo, Zhoubing Xu, Katherine Aboud, Prasanna Parvathaneni, Shunxing Bao, Camilo Bermudez, Susan M. Resnick, Laurie E. Cutting, and Bennett A. Landman. “Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data.” In International Conference on Medical Image Computing and Computer-Assisted Intervention. vol 11072. pp. 698-705 Springer, Cham, 2018. Open Access ArXiv...

Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks

Dec. 14, 2018—Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Hyeonsoo Moon, Prasanna Parvathaneni, Tamara K. Moyo et al. “Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks.” IEEE transactions on medical imaging (2018). Open Access ArXiv Download Abstract The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen...

Characterization and Correlation of Signal Drift in Diffusion Weighted MRI

Dec. 13, 2018—Colin B. Hansen, Vishwesh Nath, Allison E. Hainline, Kurt G. Schilling, Prasanna Parvathaneni, Roza G. Bayrak, Justin A. Blaber, Okan Irfanoglu, Carlo Pierpaoli, Adam W. Anderson, Baxter P. Rogers, Bennett A. Landman. “Characterization and Correlation of Signal Drift in Diffusion Weighted MRI”. Magnetic Resonance Imaging (2018). Full text: NIHMSID Abstract Diffusion weighted MRI (DWMRI) and the myriad of...

Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network

Dec. 10, 2018—Cailey I. Kerley, Yuankai Huo, Shikha Chaganti, Shunxing Bao, Mayur B. Patel, Bennett A. Landman. “Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network.” In SPIE Medical Imaging, International Society for Optics and Photonics, 2019. Full text: NIHMSID Abstract Brain imaging analysis on clinically acquired computed tomography (CT) is essential...

Machine
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learning approximat
ion of an efficacy map.
Patient MRI image overlaid with probability of positive response
assessed on a sliding patch basis (assuming an infeasible stimulation at every voxel). Green crosses indicate actual intraope
rative
stimulation locations with a positi
ve response. Blue circles indicate intraoperative stimulation locations with a null response.

Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps

Dec. 10, 2018—Bermudez, C., Rodriguez, W., Huo, Y., Hainline, A. E., Li, R., Shults, R., … & Landman, B. A. (2018). Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps. arXiv preprint arXiv:1811.10415. Full Text: https://arxiv.org/abs/1811.10415 Abstract Deep brain stimulation (DBS) has the potential to improve the quality of life of people with a...

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