Mr. M. Wasil Wahi-Anwar Profile

M. Wasil Wahi-Anwar

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Publications (9)

Proceedings Article | 2 April 2024 Presentation + Paper

Utilizing an open-source environment for cognitive-AI in feature detection and quantitative measurements of optical coherence tomography (OCT) images

Anshu Goyal, Seena Pourzand, Sachi Pawooskar-Almeida, M. Wasil Wahi-Anwar, Galo Apolo Aroca, Benjamin Xu, Matthew Brown, Brent Liu

Proceedings Volume 12931, 1293105 (2024) https://doi.org/10.1117/12.3006942

KEYWORDS: Image segmentation, Cornea, Optical coherence tomography, Iris recognition, Eye models, Education and training, Deep learning, Vacuum chambers, Image processing, Eye

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Proceedings Article | 10 April 2023 Paper

A hybrid framework of traditional and deep learning segmentation methods with feature detection for Optical Coherence Tomography (OCT) images

Anshu Goyal, Sachi Pawooskar-Almeida, M. Wasil Wahi-Anwar, Benjamin Xu, Matthew Brown, Brent Liu

Proceedings Volume 12469, 124690H (2023) https://doi.org/10.1117/12.2656170

KEYWORDS: Image segmentation, Deep learning, Image processing algorithms and systems, Education and training, Optical coherence tomography, Iris recognition, Evolutionary algorithms, Cornea, Eye, Anatomy

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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

Catheter segmentation in chest x-ray: improving imbalanced segmentation with a class frequency weighted loss function

Siyuan Wei, Youngwon Choi, M. Wasil Wahi-Anwar, Liza Shrestha, Koon-Pong Wong, Matthew Brown

Proceedings Volume 12465, 124651T (2023) https://doi.org/10.1117/12.2651992

KEYWORDS: Image segmentation, Chest imaging, Object detection, Deep learning, Image processing, Deep convolutional neural networks, X-rays

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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 | 16 February 2021 Presentation + Paper

A novel physics-based data augmentation approach for improved robust deep learning in medical imaging: lung nodule CAD false positive reduction in low-dose CT environments

M. W. Wahi-Anwar, N. Emaminejad, Y. Choi, H. Kim, W. Hsu, M. Brown, M. McNitt-Gray

Proceedings Volume 11595, 115950G (2021) https://doi.org/10.1117/12.2582126

KEYWORDS: Medical imaging, Lung, Computer aided diagnosis and therapy, Imaging systems, Range imaging, Lung cancer, Data acquisition

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Proceedings Article | 2 March 2018 Paper

The effects of variations in parameters and algorithm choices on calculated radiomics feature values: initial investigations and comparisons to feature variability across CT image acquisition conditions

Nastaran Emaminejad, Muhammad Wahi-Anwar, John Hoffman, Grace Kim, Matthew Brown, Michael McNitt-Gray

Proceedings Volume 10575, 105753W (2018) https://doi.org/10.1117/12.2293864

KEYWORDS: Computed tomography, Reconstruction algorithms, Image acquisition, Lung, Feature extraction, Lung cancer, Denoising, Cancer, Tumors, Computer aided diagnosis and therapy

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Proceedings Article | 27 February 2018 Presentation + Paper

Towards quantitative imaging: stability of fully automated nodule segmentation across varied dose levels and reconstruction parameters in a low-dose CT screening patient cohort

M. Wasil Wahi-Anwar, Nastaran Emaminejad, John Hoffman, Grace Kim, Matthew Brown, Michael McNitt-Gray

Proceedings Volume 10575, 105751L (2018) https://doi.org/10.1117/12.2293269

KEYWORDS: Image segmentation, Lung cancer, Computed tomography, Lung, Feature extraction, Computer aided diagnosis and therapy, Cancer, Image quality, Quantitative analysis

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Showing 5 of 9 publications

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