Medical-image Analysis and Statistical Interpretation (MASI) Lab

Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk

Nov. 25, 2020—Riqiang Gao, Yucheng Tang, Kaiwen Xu, Michael N. Kammer, Sanja L. Antic, Steve Deppen, Kim L. Sandler, Pierre P. Massion, Yuankai Huo, Bennett A. Landman,Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk, SPIE, Medical Imaging, 2021. Full text: https://arxiv.org/abs/2010.09524 Abstract Clinical data elements (CDEs) (e.g., age, smoking history), blood...

Multi-path x-D Recurrent Neural Networks for Collaborative Image Classification

Nov. 25, 2020—Riqiang Gao, Yuankai Huo, Shunxing Bao, Yucheng Tang, Sanja L. Antic, Emily S. Epstein, Steve Deppen, Alexis B. Paulson, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman,Multi-path x-D Recurrent Neural Networks for Collaborative Image Classification. Neurocomputing, 2020. Full Text: https://doi.org/10.1016/j.neucom.2020.02.033 Abstract With the rapid development of image acquisition and storage, multiple images per class are...

Time-Distanced Gates in Long Short-Term Memory Networks

Nov. 25, 2020—Gao, R., Tang, Y., Xu, K., Huo, Y., Bao, S., Antic, S.L., Epstein, E.S., Deppen, S., Paulson, A.B., Sandler, K.L. and Massion, P.P., Landman, B. A., Time-distanced gates in long short-term memory networks. Medical Image Analysis, 2020. Full Text: https://pubmed.ncbi.nlm.nih.gov/32745977/ Abstract The Long Short-Term Memory (LSTM) network is widely used in modeling sequential observations in fields ranging...

Figure 6. The absolute percent error (APE) in MD is shown for the human subject with one session acquired at isocenter on scanner A and another acquired 6cm superior from isocenter on scanner B. The top plot shows the sagittal and coronal view of the b0 from each session to demonstrate the shift within the scanner. The bottom plots show the APE for nine saggital slices before correction, after correction using the estimated fields, and after correction using the manufacturer specifications. The error before correction is most prominent in the superior regions of the phantom as those were the furthest from isocenter during the second acquisition.

Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI

Nov. 25, 2020—Colin B. Hansen, Baxter P. Rogers, Kurt G. Schilling, Vishwesh Nath, Justin A. Blaber, Okan Irfanoglu, Alan Barnett, Carlo Pierpaoli, Adam W. Anderson, Bennett A. Landman. “Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI.” Magnetic Resonance Imaging 2020. Full Text Abstract Background: Achieving inter-site / inter-scanner reproducibility of diffusion weighted magnetic resonance...

Figure 1. Renal structures segmentation of clinically acquired CT image. Yellow box shows a representative right kidney, green box shows the left kidney. The zoom-in patches show the CT kidney field-of-view (FOV) and labels. The label patches display the renal cortex (blue), the medulla (red) and the pelvicalyceal system (green). Those small structures make automatic renal segmentation challenging.

Renal Cortex, Medulla and Pelvicaliceal System Segmentation on Arterial Phase CT Images with Random Patch-based Networks

Nov. 21, 2020—Yucheng Tang, Riqiang Gao, Ho Hin Lee, Brent V. Savoie, Shunxing Bao, Yuankai Huo, Jeffrey Spraggins and Bennett A, Landman, Renal Cortex, Medulla, Pelvis Segmentation on Arterial Phase CT Images with Random Patch-based Networks, SPIE 2021 Medical Imaging Full Text Abstract Renal segmentation on contrast-enhanced computed tomography (CT) provides distinct spatial context and morphology. Current studies for renal segmentations...

Joint cortical surface and structural connectivity analysis of Alzheimer’s Disease

Nov. 20, 2020—Leon Y. Cai, Cailey I. Kerley, Chang Yu, Katherine S. Aboud, Lori L. Beason-Held, Andrea T. Shafer, Susan M. Resnick, Lori C. Jordan, Adam W. Anderson, Kurt G. Schilling, Ilwoo Lyu, Bennett A. Landman. Joint cortical surface and structural connectivity analysis of Alzheimer’s Disease. SPIE Medical Imaging, 2021. Full Text: NIHMSID Abstract Joint independent component...

Joint analysis of structural connectivity and cortical surface features: correlates with mild traumatic brain injury

Nov. 18, 2020—Cailey I. Kerley, Leon Y. Cai, Chang Yu, Logan M. Crawford, Jason M. Elenberger, Eden S. Singh, Kurt G. Schilling, Katherine S. Aboud, Bennett A. Landman, Tonia S. Rex, “Joint analysis of connectivity and cortical surface features: correlates with mild traumatic brain injury.” (2021, Feb) SPIE Medical Imaging. San Diego, CA. (Accepted) Full text: NIHMSID, arXiv...

Validation of group-wise registration for surface-based functional MRI analysis

Oct. 20, 2020—Chang Yu, Yue Liu, Leon Cai, Cailey Kerley, Kaiwen Xu, Katherine Aboud, Warren Taylor, Hakmook Kang, Andrea Shafer, Lori Beason-Held, Susan Resnick, Bennett Landman, Ilwoo Lyu. “Validation of group-wise registration for surface-based functional MRI analysis”. SPIE Medical Imaging 2021. [Full text][Code] Abstract Resting-state functional MRI (rsfMRI) provides important information for studying and mapping the activities...

Qualitative comparison of tracer and tractography for PCG injection. Tracer digitized on the reference atlas [and reproduced from Schmahmann and Pandya (2006), with permission] is shown alongside the anatomically matched b0 slice with streamlines shown in black (only streamlines within ± 1 slice are displayed) for both the manual-based dissection and the template-based dissection

Brain connections derived from diffusion MRI tractography can be highly anatomically accurate – if we know where white matter pathways start, where they end, and where they don’t go

Aug. 25, 2020—Kurt G Schilling, Laurent Petit, Francois Rheault, Samuel Remedios, Carlo Pierpaoli, Adam W Anderson, Bennett A Landman, Maxime Descoteaux. “Brain connections derived from diffusion MRI tractography can be highly anatomically accurate – if we know where white matter pathways start, where they end, and where they don’t go”. Brain Struct Funct (2020). https://doi.org/10.1007/s00429-020-02129-z Full text: https://link.springer.com/article/10.1007/s00429-020-02129-z Abstract MR...

Figure 4. The response function varies across brain regions. Three locations in the same monkey brain (monkey #2) are shown, along with the histological fiber orientation distribution, signal, and derived response function for all 9 voxels. In addition, the average response function for each z-stack is shown in two views.

Histologically derived fiber response functions for diffusion MRI vary across white matter fibers – an ex vivo validation study in the squirrel monkey brain

Aug. 25, 2020—Kurt G Schilling, Yurui Gao, Iwona Stepniewska, Vaibhav Janve, Bennett A. Landman, Adam W. Anderson. “Histologically derived fiber response functions for diffusion MRI vary across white matter fibers – an ex vivo validation study in the squirrel monkey brain”. NMR in Biomedicine. 2019 Jun;32(6):e4090. doi: 10.1002/nbm.4090. Epub 2019 Mar 25. Full text: https://pubmed.ncbi.nlm.nih.gov/30908803/ Abstract Understanding the...

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