VLST: Virtual Lung Screening Trial for Lung Cancer Detection Using Virtual Imaging Trial

no code implementations • 17 Apr 2024 • Fakrul Islam Tushar, Liesbeth Vancoillie, Cindy McCabe, Amareswararao Kavuri, Lavsen Dahal, Brian Harrawood, Milo Fryling, Mojtaba Zarei, Saman Sotoudeh-Paima, Fong Chi Ho, Dhrubajyoti Ghosh, Sheng Luo, W. Paul Segars, Ehsan Abadi, Kyle J. Lafata, Ehsan Samei, Joseph Y. Lo

Objectives: To establish a virtual imaging trial (VIT) platform that accurately simulates real-world lung screening trials (LSTs) to assess the diagnostic accuracy of CT and CXR modalities.

Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging

1 code implementation • 17 Aug 2023 • Fakrul Islam Tushar, Lavsen Dahal, Saman Sotoudeh-Paima, Ehsan Abadi, W. Paul Segars, Ehsan Samei, Joseph Y. Lo

In this study, COVID-19 serves as a case example to unveil the intrinsic and extrinsic factors influencing AI performance.

Computed Tomography (CT) COVID-19 Diagnosis

Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography

no code implementations • 7 Mar 2022 • Fakrul Islam Tushar, Ehsan Abadi, Saman Sotoudeh-Paima, Rafael B. Fricks, Maciej A. Mazurowski, W. Paul Segars, Ehsan Samei, Joseph Y. Lo

However, performance dropped to an AUC of 0. 65 and 0. 69 when evaluated on clinical and our simulated CVIT-COVID dataset.

Computed Tomography (CT) COVID-19 Diagnosis

Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT

no code implementations • 3 Mar 2022 • Fakrul Islam Tushar, Husam Nujaim, Wanyi Fu, Ehsan Abadi, Maciej A. Mazurowski, Ehsan Samei, William P. Segars, Joseph Y. Lo

This demonstrates that quality data is the key to improving the model's performance.

Organ Segmentation Segmentation

iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry

no code implementations • 20 Aug 2020 • Wanyi Fu, Shobhit Sharma, Ehsan Abadi, Alexandros-Stavros Iliopoulos, Qi. Wang, Joseph Y. Lo, Xiaobai Sun, William P. Segars, Ehsan Samei

Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical images.