Medical-image Analysis and Statistical Interpretation (MASI) Lab

Violin plots of intrasession submissions across both the scanners per tract. (A) Dice similarity coefficients, (B) intraclass correlation coefficients. The top row depicts the median of the top five intrasession submissions. The tracts are in the following order (L/R): a) Uncinate, b) Fornix, c) Fminor & Fmajor, d) Cingulum, e) Corticospinal tract, f) Inferior longitudinal fasciculus, g) Superior longitudinal fasciculus, h) Inferior fronto‐occipital tract.

Tractography reproducibility challenge with empirical data (TRAceD): The 2017 ISMRM diffusion study group challenge

Jan. 17, 2020—Nath V, Schilling KG, Parvathaneni P, Huo Y, Blaber JA, Hainline AE, Barakovic M, Romascano D, Rafael‐Patino J, Frigo M, Girard G. Tractography reproducibility challenge with empirical data (traced): The 2017 ISMRM diffusion study group challenge. Journal of Magnetic Resonance Imaging. 2020 Jan;51(1):234-49. Full text: https://www.ncbi.nlm.nih.gov/pubmed/31179595 Abstract BACKGROUND: Fiber tracking with diffusion-weighted MRI has become an...

A) Middle axial slice of a human brain acquired using DW-MRI. B, C) CSD FOD's of the same subject on two different scanners showing inconsistency which cannot be validated without ground truth. D) Coronal slice of a Squirrel Monkey. E, F, G, H) Right: CSD FOD's at b-value of 3000 s/mm2 Left: Ground truth reconstruction using aggregated histological structural tensors depicting a loss in precision.

Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI

Jan. 17, 2020—Nath V, Schilling KG, Parvathaneni P, Hansen CB, Hainline AE, Huo Y, Blaber JA, Lyu I, Janve V, Gao Y, Stepniewska I, Anderson AW, Landman BA. Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI. Magnetic resonance imaging. 2019 Oct 1;62:220-7. Abstract PURPOSE: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance...

Figure 2

Illustration of multiple instance learning with gradient accumulation. The first CT volume is organized into a bag of its 2D slices. Then, the model performs inference on all slices in the bag, and class probabilities are calculated. The gradient is calculated only for the instance corresponding to the most probable positive class. This gradient is saved and the gradient calculation is repeated for the next bag until the batch is done, and the accumulated gradient is averaged for the batch size and applied to the model.

Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection

Jan. 2, 2020—Remedios, S. W., Wu, Z., Bermudez, C., Kerley, C. I., Roy, S., Patel, M. B., Butman, J. A., Landman, B. A., Pham, D. L. (2019). Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection. arXiv preprint arXiv:1911.05650. Full Text: Arxiv Link Abstract Multiple instance learning (MIL) is a supervised learning methodology...

Distributed Deep Learning Across Multisite Datasets for Generalized CT Hemorrhage Segmentation

Jan. 2, 2020—Remedios, S. W., Roy, S., Bermudez, C., Patel, M. B., Butman, J. A., Landman, B. A., & Pham, D. L. (2019). Distributed Deep Learning Across Multi‐site Datasets for Generalized CT Hemorrhage Segmentation. Medical physics. Full Text: Pubmed Link Abstract Purpose: As deep neural networks achieve more success in the wide field of computer vision, greater emphasis is...

Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury

Jan. 2, 2020—Remedios, Samuel, et al. “Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury.” Medical Imaging 2019: Image Processing. Vol. 10949. International Society for Optics and Photonics, 2019. Full text: PubMed Link Abstract Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need...

Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

Dec. 19, 2019—Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman, “Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision”, SPIE MI:IP 2020. Houston, TX. Link: https://arxiv.org/abs/1911.05113 Abstract Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that...

Diffusion MRI microstructural models in the cervical spinal cord – application, normative values, and correlations with histological analysis

Dec. 16, 2019—Kurt G. Schilling, Samantha By, Haley Feiler, Bailey Box, Kristin P. O’Grady, Atlee Witt, Bennett A. Landman, Seth A. Smith. “Diffusion MRI microstructural models in the cervical spinal cord – application, normative values, and correlations with histological analysis”. NeuroImage. doi: 10.1016/j.neuroimage.2019.116026. 2019. Full text: https://www.ncbi.nlm.nih.gov/pubmed/31326569 Abstract Multi-compartment tissue modeling using diffusion magnetic resonance imaging has proven...

Fig. 2. Distortion and correction in diffusion MRI. (A) EPI susceptibility distortion occurs along the phase encode direction, with phase encoding in the posterior (PE-P) direction leading to displacements of identical distance but opposite direction of that in the anterior (PE-A) direction. (B) State of the art distortion correction (topup) typically uses distortions in two opposite directions to iteratively estimate the undistorted image. (C) The proposed method uses an undistorted T1-weighted image to synthesize an undistorted volume with b0 contrast, which can be used to correct the distorted (in this case, PE-P) image without requiring an additional phase encoding acquisition. The blue circles highlight areas of observable signal distortion. Yellow arrows indicate phase encode direction. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Synthesized b0 for diffusion distortion correction (Synb0-DisCo).

Dec. 16, 2019—Kurt G Schilling, Yuankai Huo, Allen Newton, Colin Hansen, Vishwesh Nath, Andrea T. Shafer, Owen Williams, Susan M. Resnick, Baxter Rogers, Adam W Anderson, Bennett A Landman. “Synthesized b0 for diffusion distortion correction (Synb0-DisCo).” Magnetic Resonance Imaging. 2019 May 7. pii: S0730-725X(18)30617-9. doi: 10.1016/j.mri.2019.05.008 Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Synthesized+b0+for+diffusion+distortion+correction+(Synb0-DisCo). Abstract Diffusion magnetic resonance images typically suffer from spatial...

MRI correlates of chronic symptoms in mild traumatic brain injury

Dec. 6, 2019—Kerley, C. I., Schilling, K. G., Blaber, J., Miller, B., Newton, A., Anderson, A. W., Landman, B. A., Rex, T. S. “MRI correlates of chronic symptoms in mild traumatic brain injury.” In SPIE Medical Imaging, International Society for Optics and Photonics, 2020. Full text: NIHMSID, arXiv Abstract Some veterans with a history of mild traumatic brain injury (mTBI) have reported experiencing...

Qualitative results of the DN segmentation. Here, we show the output of the automatic segmentation using unimodal U-Net, a multisequence U-Net, and single atlas registration of the SUIT template in the same subject. The manual label is shown in green while the predicted label is shown in red. Overlap between the two structures is shown in yellow. The Dice coefficient is 0.95 for the unimodal T1 model, 0.95 for the unimodal T2 model, 0.97 for the unimodal FA model, 0.93 for the multisequence model, 0.41 for the SUIT atlas, and 0.43 for the multi-atlas segmentation. Note: the multiatlas segmentation results were registered to the SUIT template for visualization.

Using deep learning for a diffusion-based segmentation of the dentate nucleus and its benefits over atlas-based methods

Dec. 6, 2019—Noguera, C. B., Bao, S., Petersen, K. J., Lopez, A. M., Reid, J., Plassard, A. J., … & Landman, B. A. (2019). Using deep learning for a diffusion-based segmentation of the dentate nucleus and its benefits over atlas-based methods. Journal of Medical Imaging, 6(4), 044007. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/31824980 Abstract The dentate nucleus (DN) is a...

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