Computed Tomography Category

A representative subject was evaluated with URN (left) and BUSN (right). Green scatters are inliers of influence to the regression, yellow
scatters are outliers of no influence to the distributed data. Darker blue line indicates the normal linear regression on scatters points, lighter blue
line is the RANSAC regressor result according to inliers. Left panel presents the single URN regression with amounts of outliers result in failure of
linearity nature in chest and pelvis regions. Right panel shows the testing result of BUSN method, the distributed scores follows good linearity in
chest, abdomen and pelvis regions in CT scan. In summary, BUSN takes advantage of self-supervised network, which presents better continuity in
regression result among neighbor slices and shows scatter plots without number of outliers.

Body Part Regression With Self-Supervision

Jan. 14, 2021—Y.Tang, R.Gao, S.Han, Y.Chen, D.Gao, V.Nath, C.Bermudez, M.R. Savona, R.G. Abramson, S.Bao,I.Lyu, Y.Huo and B.A. Landman,”Body Part Regression with Self-supervision”,IEEETransactions onMedicalImaging,2021 Full Text: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9350603 Abstract Body part regression is a promising new technique that enables content navigation through selfsupervised learning. Using this technique, the global quantitative spatial location for each axial view slice is obtained...

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Multi-Contrast Computed Tomography Healthy Kidney Atlas

Dec. 28, 2020—Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes B. Fogo, Raymond Harris, Mark P. de Caestecker, Mattias Heinrich, Jeffery M. Spraggins, Yuankai Huo, Bennett A. Landman, “Multi-Contrast Computed Tomography Healthy Kidney Atlas.” arXiv preprint arXiv:2012.12432 (2020). Full Text Abstract The construction of three-dimensional multi-modal tissue maps provides an opportunity to spur interdisciplinary innovations across temporal and...

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records

Dec. 7, 2020—Yucheng Tang, Riqiang Gao, Ho Hin Lee, Quinn Stanton Wells, Ashley Spann, James Gregory Terry, Jeff Carr, Yuankai Huo, Shunxing Bao and Bennett A. Landman, “Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records”, MICCAI CLIP, 2020. Full Text: Abstract Type II diabetes mellitus (T2DM) is a significant public health concern...

Learning from dispersed manual annotations with an optimized data weighting policy

Dec. 7, 2020—Yucheng Tang, Riqiang Gao, Yunqiang Chen, Dashan Gao, Michael R. Savona, Richard G. Abramson, Shunxing Bao, Yuankai Huo and Bennett A. Landman, “Learning from Dispersed Manual Annotations with an Optimized Data Weighting Policy”, Journal of Medical Imaging, 2020. Full Text: Abstract https://pubmed.ncbi.nlm.nih.gov/32775501/ Purpose: Deep learning methods have become essential tools for quantitative interpretation of medical...

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Construction of a Multi-Phase Contrast Computed Tomography Kidney Atlas

Dec. 6, 2020—Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes B. Fogo, Raymond Harris, Mark P. de Caestecker, Mattias Heinrich, Jeffrey Spraggins, Yuankai Huo, Bennett A, Landman, Construction of a Multi-Phase Contrast Computed Tomography Kidney Atlas, SPIE 2021 Medical Imaging Full Text Abstract The Human BioMolecular Atlas Program (HuBMAP) seeks to create a molecular atlas at the cellular level of...

A cross-platform informatics system for the Gut Cell Atlas: integrating from clinical, anatomical and histological data

Dec. 6, 2020—Shunxing Bao, Sophie Chiron, Yucheng Tang, Cody N. Heiser, Austin N. Southard-Smith, Ho Hin Lee, Marisol A. Ramirez, Yuankai Huo, Mary K. Washington, Elizabeth A. Scoville, Joseph T. Roland, Qi Liu, Ken S. Lau, Keith T. Wilson, Lori A. Coburn, Bennett A. Landman, A cross-platform informatics system for the Gut Cell Atlas: integrating from clinical,...

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...

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...

Figure. Four consecutive modules of the atlas creation pipeline. (A) Preprocessing module: (A.1) original moving scan; (A.2) the overlap between the ambient removed scan and lung & body segmentation masks. (B) Affine registration module based on NiftyReg toolbox: (B.1) moving scan with intensity window (0, 1000); (B.2) axial view of reference scan; (B.3) reference scan with intensity window (0, 1000); (B.4) affine registered intensity windowed moving scan; (B.5) interpolation of the preprocessed moving scan with the affine registration transformation matrix. (C) Non-rigid registration module based on corrField (registration) and NiftyReg (interpolation): (C.1) affine registered moving scan overlapped with lung and body segmentation masks; (C.2) overlapped with non-NaN region mask; (C.3) reference scan with non-NaN region mask; (C.4) non-rigid deformed moving scan overlapped with deformed non-NaN region mask; (C.5) non-rigid deformed moving scan overlapped with deformed lung / body masks. (D) Atlas creation module: (D.1) average atlas of HU; (D.2) average atlas of Jacobian determinant map.

Development and Characterization of a Chest CT Atlas

Oct. 13, 2020—Kaiwen Xu, Riqiang Gao, Mirza S. Khan, Shunxing Bao, Yucheng Tang, Steve A. Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Mattias P. Heinrich, Bennett A. Landman. Development and characterization of a chest CT atlas. SPIE Medical Imaging, 2021. Full text: https://arxiv.org/abs/2012.03124 Abstract A major goal of lung cancer screening is to identify individuals...

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...

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