Dr. Steven S. Raman Profile

Dr. Steven S. Raman

at Univ of California Los Angeles

SPIE Involvement:

Author

Publications (5)

Proceedings Article | 7 April 2023 Poster + Paper

Reducing dose in renal perfusion CT scans: the effects on quantitative imaging features for patients of different sizes

Morgan Daly, M. Wasil Wahi-Anwar, Gabriel Melendez-Corres, Grace Hyun J. Kim, Steven Raman, Michael McNitt-Gray

Proceedings Volume 12463, 1246321 (2023) https://doi.org/10.1117/12.2651479

KEYWORDS: Computed tomography, Perfusion imaging, Image quality, Feature extraction, Tumors, Kidney, Image analysis, Visualization, Radiomics, Clinical medicine

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Proceedings Article | 7 April 2023 Poster + Paper

Machine reasoning for segmentation of the kidneys on CT images: improving CNN performance by incorporating anatomical knowledge in post-processing

G. Melendez-Corres, Y. Choi, M. Wahi-Anwar, H. Coy, S. Raman, M. Brown

Proceedings Volume 12465, 124651U (2023) https://doi.org/10.1117/12.2652257

KEYWORDS: Kidney, Image segmentation, Anatomy, Spine, Abdomen, Voxels, Computed tomography, Artificial intelligence

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Purpose: Convolutional Neural Networks (CNN) are frequently used for organ segmentation in medical imaging. Many CNNs, however, struggle for acceptance into clinical practice because of mis-segmentations that are obvious to radiologists or other experts. We propose using a Cognitive AI framework that applies anatomical knowledge and machine reasoning to check and improve CNN segmentations and avoid obvious mis-segmentations. Methods: We used the open-source SimpleMind framework that allows post-processing and machine reasoning to be applied to CNN segmentation results. Within this framework, a 3D-UNet CNN was trained on 212 contrast-enhanced kidney CT scans. From an anatomical knowledge base, SimpleMind derived the following Cognitive AI post-processing steps: (1) identification of the abdomen, (2) segmentation of the spine by searching for bone in the posterior region of the abdomen, (3) CNN output separation into right and left kidneys using the spine as reference, (4) refinement of individual kidney segmentations using thresholds of volume, HU, and voxel count, which are designed to eliminate disconnected false positives, (5) morphological opening to reduce connected false positives bordering the kidneys’ segmentations. On a test set of 53 scans, using reference annotations, Dice Coefficient (DC), Hausdorff Distance (HD) and Average Symmetric Surface Distances (ASSD) were computed and compared for the CNN and post-processed outputs. Results: Post-processing with Cognitive AI reduced the kidney segmentation HD from 46.4±55.8 mm to 30.7±17.6 mm, with the decrease in variance being statistically significant (p = 0.0296). DC and ASSD metrics were also improved. Conclusions: This initial work demonstrates that the incorporation of anatomical knowledge using Cognitive AI techniques can improve the segmentation accuracy of CNNs. The CNN provides very good overall kidney segmentation performance, and in cases where segmentation errors occur the post processing was able to improve performance.

Proceedings Article | 3 April 2023 Poster + Paper

Using a GAN for CT contrast enhancement to improve CNN kidney segmentation accuracy

Spencer Welland, Gabriel Melendez-Corres, Pangyu Teng, Heidi Coy, Muhammad Wahi-Anwar, Steve Raman, Matthew Brown

Proceedings Volume 12464, 124642J (2023) https://doi.org/10.1117/12.2654038

KEYWORDS: Image segmentation, Kidney, Image contrast enhancement, Computed tomography, Visualization, Tumors, Medical imaging, Image quality

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Proceedings Article | 4 April 2022 Poster + Paper

AI-human interactive pipeline with feedback to accelerate medical image annotation

Youngwon Choi, Marlena Garcia, Steven Raman, Dieter Enzmann, Matthew Brown

Proceedings Volume 12033, 120332U (2022) https://doi.org/10.1117/12.2611952

KEYWORDS: Artificial intelligence, Image analysis, Image segmentation, Prostate, Process modeling, Medical imaging, Feedback loops

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SPIE Journal Paper | 29 December 2020

Semi-automated PIRADS scoring via mpMRI analysis

Nikhil Dhinagar, William Speier, Karthik Sarma, Alex Raman, Adam Kinnaird, Steven Raman, Leonard Marks, Corey Arnold

JMI, Vol. 7, Issue 06, 064501, (December 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.6.064501

KEYWORDS: Performance modeling, Data modeling, Principal component analysis, Prostate, Magnetic resonance imaging, Statistical modeling, Biopsy, Statistical analysis, Data centers, Cancer

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