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Accelerating 2D Abdominal Organ Segmentation with Active Learning

Dec. 10, 2021—Xin Yu, Yucheng Tang, Qi Yang, Ho Hin Lee, Shunxing Bao, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman Abdominal computed tomography CT imaging enables assessment of body habitus and organ health. Quantification of these health factors necessitates semantic segmentation of key structures. Deep learning efforts have shown remarkable success in automating segmentation of...

Author Guidelines for 8 - Adobe Acrobat Reader DC 12_28_2020 5_49_47 PM (2)

RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior

Dec. 28, 2020—Ho Hin Lee, Yucheng Tang, Shunxing Bao, Richard G. Abramson, Yuankai Huo, Bennett A. Landman. “RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior.” arXiv preprint arXiv:2012.12425 (2020). Full Text Abstract Performing coarse-to-fine abdominal multi-organ segmentation facilitates to extract high-resolution segmentation minimizing the lost of spatial contextual information. However, current coarse-to-refine approaches require a significant number of models...

For experiment 1 using HAM10000, the mean balanced multiclass accuracy across five folds is shown for the hyperparameter search for MixMatch (bottom left) and Nullspace Tuning (top left). The highest performing hyperparameter is used in reporting the final performance (right) where the baseline is the balanced multiclass accuracy reported by the ISIC 2018 challenge for the Li method. The shaded region represents the standard error of the mean.

Semi-supervised Machine Learning with MixMatch and Equivalence Classes

Dec. 4, 2020—Colin B. Hansen, Vishwesh Nath, Riqiang Gao, Camilo Bermudez, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Jeffrey D. Blume, Thomas A. Lasko, Bennett A. Landman “Semi-supervised Machine Learning with MixMatch and Equivalence Classes.” Interpretable and Annotation-Efficient Learning for Medical Image Computing. Springer, Cham, 2020. 112-121. Full Text Abstract Semi-supervised methods have an increasing impact on...

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...

Fig 8. External dataset validation.
External validation of corrected b0’s after applying the synthesized b0 distortion correction pipeline with data from open-sourced studies. The distorted (“D”) and undistorted (“U”) b0 images are shown alongside T1 images. In all cases, effective distortion correction is visually apparent (distortions indicated by arrows).

Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

Aug. 25, 2020—Kurt G Schilling*, Justin Blaber*, Colin Hansen, Leon Cai, Baxter Rogers, Adam W Anderson, Seth A Smith, Praitayini Kanakaraj, Tonia Rex, Susan M. Resnick, Andrea T. Shafer, Laurie Cutting, Neil Woodward, David Zald, Bennett A Landman.“ Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps ”. PLoS ONE 15(7): e0236418. https://doi.org/10.1371/journal.pone.0236418 Full text: https:...

Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort

Apr. 20, 2020—Lingyan Hao, Shunxing Bao, Yucheng Tang, Riqiang Gao, Prasanna Parvathaneni, Jacob Miller, Willa Voorhies, Jewelia Yao, Silvia Bunge, Kevin Weiner, Bennett Landman, Ilwoo Lyu. “Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort”. IEEE International Symposium on Biomedical Imaging (ISBI) 2020, IEEE, 412-415, Iowa City, Iowa, USA, 2020. [Full text][Code] Abstract...

Examples of segmentations generated by the baseline algorithm. A. An inlier, where the algorithm correctly predicted larger organs, like the liver (on the left side of image in purple) and spleen (on the right side of image in red- pink) and suggested mostly accurate areas of smaller organs. B. An outlier (global failure), where the liver (purple) and spleen (red-pink) were largely correct but major inconsistencies are visible with other organs.

Outlier Guided Optimization of Abdominal Segmentation

Feb. 12, 2020—Yuchen Xu*, Olivia Tang*, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman, “Outlier Guided Optimization of Abdomen Segmentation”, SPIE IP:MI 2020. Houston, TX https://arxiv.org/abs/2002.04098 Abstract Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive...

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...

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