‘CT’

Figure 3. We perform kernel conversion on paired hard and soft CT kernel images using a pix2pix GAN. The generator, a U-Net, takes as input the source CT kernel image and predicts a residue image. The output kernel is obtained by adding the residue image to the input source kernel image. The discriminator, a PatchGAN, takes as input the generated image and the input kernel, and outputs the probability of the generated image being a translated version of the input.

Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks

Apr. 24, 2024—Aravind R. Krishnan, Kaiwen Xu, Thomas Li, Lucas W. Remedios, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman. “Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.”Med Phys. 2024;1-14.https://doi.org/10.1002/mp.17028 Abstract Background The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction...

Representative images are predicted to associate with comorbidities and ICD-10 codes
(phenotype components) identified in each risk category. The red outlines show the pancreas
tissue can be different under phenotyping contexts. (1) is from a nominally healthy pancreas
group with potential lung infections; (2) is from type I diabetes and other chronic kidney disease
patients with atrophic pancreas; (3) is from other metabolic syndromes including type II diabetes;
(4) is from patients with weight loss and pancreatitis.

Pancreas CT Segmentation by Predictive Phenotyping

Dec. 14, 2021—Y. Tang, R.Gao, H.H.Lee, Q.Yang, X.Yu,Y.Zhou, S.Bao, Y.Huo, J.Spraggins, J.Virostko, Z.Xu, B.A. Landman. “Pancreas CTSegmentation by Predictive Phenotyping”. International Conference on MedicalImage Computing and Computer Assisted Intervention(MICCAI), 2021 Full Text: https://link.springer.com/chapter/10.1007/978-3-030-87193-2_3 Abstract Pancreas CT segmentation offers promise at understanding the structural manifestation of metabolic conditions. To date, the medical primary record of conditions that impact...

Method framework. Given a CT scan with at high resolution of ~0.8 x ~0.8 x ~2 mm, a low-res section (left panel) is trained with multi-channel segmentation. The low-res part contains down-sampling and normalization in order to preserve the complete spatial information. After the coarse segmentations are acquired from low- res UNet, we interpolate the mask to match the image’s original resolution. Next, random patch sampling (mid panel) is employed to collect patches, and patches are concatenated with corresponding coarse segmentation masks. Finally, we trained a patch-based high-res (right panel) segmentation model, the high-dimensional probability maps are acquired from integration of all patches on field of views. Majority vote is used to merge estimates into a final segmentation.

High-resolution 3D abdominal segmentation with random patch network fusion

Dec. 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,“High-resolution 3D Abdominal Segmentation with Random PatchNetworkFusion”,Medical Image Analysis, 2021. Full Text: https://www.sciencedirect.com/science/article/pii/S1361841520302589 Abstract Deep learning for three dimensional (3D) abdominal organ segmentation on high-resolution computed to- mography (CT) is a challenging topic, in part due to the limited memory provide...

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...

Figure 3
Flowchart of the proposed method. The atlas labels are co-registered to construct a pose-free implicit parametric shape model, including the mean and the modes of variation of the spleen shape. The atlas images are registered to the target image, based on which the atlas labels are propagated to the target space. The locally weighted vote yields the initial fusion result of the registered atlas labels weighted by the local intensity similarity between the registered atlas images and the target image in the form of a fuzzy estimate of label probability and a binary estimate of spleen segmentation. The binary image of the mean shape from the pre-constructed spleen shape model is registered to that of the current estimate with two distinct effective ranges, i.e., (1) the whole volume of both image and (2) the mean shape region, of the similarity metric of registration so that the pose-free shape model is transformed into the target space. The current estimate of the spleen is then projected to the two registered shape models. The shape projections are converted into probabilistic priors to adjust the label probability from locally weighted vote, and then generate a new estimate of the spleen. The estimate can be refined with iterative adjustment.

Shape-Constrained Multi-Atlas Segmentation of Spleen in CT

Feb. 1, 2014—Z. Xu, B. Li, S. Panda, A. Asman, Kristen. L. Merkle, Peter L. Shanahan, Richard G. Abramson, B. Landman. “Shape-Constrained Multi-Atlas Segmentation of Spleen in CT.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014† Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Shape-Constrained+Multi-Atlas+Segmentation+of+Spleen+in+CT Abstract Spleen segmentation on clinically acquired CT data is a challenging problem given...