Computed Tomography Category

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...

A cycle GAN consists of a forward and backward path. In the forward path, the source encoder and target decoder combine together to form a U-Net that generates a synthetic image with the style of the target domain. The synthetic image and the real target domain image are passed as inputs to a discriminator DB which distinguishes whether the generated image is real or fake. In the backward path, a synthetic image with the style of the source domain is generated which is fed to discriminator DA along with the source domain image.

Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation

Dec. 1, 2023—Aravind R. Krishnan, Kaiwen Xu, Thomas Li, Chenyu Gao, Lucas W. Remedios, Praitayini Kanakaraj, Ho Hin Lee, Shunxing Bao, Kim L. Sandler, Fabien Maldonado, Ivana Išgum, and Bennett A. Landman “Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation”, Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129261D (2 April 2024); https://doi.org/10.1117/12.3006608 Abstract The reconstruction kernel in computed tomography (CT) generation determines the texture of the image. Consistency...

Characterizing Low-cost Registration for Photographic Images to Computed Tomography

Dec. 1, 2023—Michael E. Kim, Ho Hin Lee, Karthik Ramadass, Chenyu Gao, Katherine Van Schaik, Eric Tkaczyk, Jeffrey Spraggins, Daniel C. Moyer, Bennett A. Landman Biorxiv Link: https://www.biorxiv.org/content/10.1101/2023.09.22.558989v1 Figure 1. We map surfaces of phantom objects obtained from low-cost photogrammetry to surfaces obtained from volumetric CT scans of the phantoms in order to examine if similar...

Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model

Aug. 31, 2023—Gao R, Li T, Tang Y, Xu K, Khan M, Kammer M, Antic SL, Deppen S, Huo Y, Lasko TA, Sandler KL, Maldonado F, Landman BA Paper: https://pubmed.ncbi.nlm.nih.gov/36198225/ Code: https://github.com/MASILab/STrUDeL Abstract Objective: Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image...

Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation

Aug. 31, 2023—Ho Hin Lee, Yucheng Tang, Qi Yang, Xin Yu, Leon Y. Cai, Lucas W. Remedios, Shunxing Bao, Bennett A. Landman, Yuankai Huo Paper: https://ieeexplore.ieee.org/document/10149329 Code: https://github.com/MASILab/DCC_CL Abstract Medical image segmentation, or computing voxel-wise semantic masks, is a fundamental yet challenging task in medical imaging domain. To increase the ability of encoder-decoder neural networks to perform this task...

Extending the value of routine lung screening CT with quantitative body composition assessment

Nov. 28, 2022—Kaiwen Xu, Riqiang Gao, Yucheng Tang, Steve A. Deppen, Kim L. Sandler, Michael N. Kammer, Sanja L. Antic, Fabien Maldonado, Yuankai Huo, Mirza S. Khan, Bennett A. Landman Abstract Certain body composition phenotypes, like sarcopenia, are well established as predictive markers for post-surgery complications and overall survival of lung cancer patients. However, their association with...

3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation

Oct. 6, 2022—Ho Hin Lee, Shunxing Bao, Yuankai Huo, Bennett A. Landman, “3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation”, arXiv 2022 Full Text Abstract Vision transformers (ViTs) have quickly superseded convolutional networks (ConvNets) as the current state-of-the-art (SOTA) models for medical image segmentation. Hierarchical transformers (e.g., Swin Transformers) reintroduced several...

Overview of our proposed pre-training framework.
Input CT images are randomly cropped into sub-volumes and
augmented with random inner cutout and rotation, then fed to the
Swin UNETR encoder as input. We use masked volume inpainting,
contrastive learning and rotation prediction as proxy tasks for
learning contextual representations of input images

Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

Jul. 25, 2022—Tang, Yucheng, Dong Yang, Wenqi Li, Holger R. Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh. “Self-supervised pre-training of swin transformers for 3d medical image analysis.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20730-20740. 2022. Full text: Abstract Vision Transformers (ViT)s have shown great performance in self-supervised...

Each image volume from the mixed CT dataset is windowed for blood (width 130 Hounsfield units, level 50 Hounsfield
units), converted into a 2D montage from resampled axial slices and normalized. A multitask deep learning model then
simultaneously performs two classifications: CT vs CTA and artifact vs no artifact.

Efficient Quality Control with Mixed CT and CTA Datasets

Dec. 10, 2021—Lucas W. Remedios, Leon Y. Cai, Colin B. Hansen, Samuel W. Remedios, Bennett A. Landman (2022). Efficient Quality Control with Mixed CT and CTA Datasets. Proc SPIE Int Soc Opt Eng. 2022. Abstract Deep learning promises the extraction of valuable information from traumatic brain injury (TBI) datasets and depends on efficient navigation when using large-scale mixed computed tomography (CT) datasets from...

A comparison of the number of learnable parameters and the types and number of skip connections in our five models. Note that the skip connections type column lists operations which show how residual information is reintegrated when the skip / residual connection or parallel processing branch rejoins the network.

Technical Note: Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography

Jul. 21, 2021—Lucas W. Remedios, Sneha Lingam, Samuel W. Remedios, Riqiang Gao, Stephen W. Clark, Larry T. Davis, Bennett A. Landman. Technical Note: Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography. Medical Physics, 2021 Full Text Abstract Purpose: Artificial intelligence diagnosis and triage of large vessel occlusion may quicken clinical response for...

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