no code implementations • 14 Feb 2024 • Andrew M. Nguyen, Jianfei Liu, Tejas Sudharshan Mathai, Peter C. Grayson, Ronald M. Summers
Heart, aorta, and lung segmentations were determined using TotalSegmentator, while plaques in the coronary arteries and heart valves were manually labeled for 801 volumes.
no code implementations • 12 Feb 2024 • Kimberly Helm, Tejas Sudharshan Mathai, Boah Kim, Pritam Mukherjee, Jianfei Liu, Ronald M. Summers
In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis.
no code implementations • 12 Feb 2024 • David C. Oluigboa, Bikash Santra, Tejas Sudharshan Mathai, Pritam Mukherjee, Jianfei Liu, Abhishek Jha, Mayank Patel, Karel Pacak, Ronald M. Summers
Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and extra-adrenal tumors which have the potential to metastasize.
no code implementations • 31 Jan 2024 • Tao Sheng, Tejas Sudharshan Mathai, Alexander Shieh, Ronald M. Summers
First, we used the bone lesions that were prospectively marked by radiologists in a few 2D slices of CT volumes and converted them into weak 3D segmentation masks.
no code implementations • 29 Jan 2024 • Qingqing Zhu, Xiuying Chen, Qiao Jin, Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee, Xin Gao, Ronald M Summers, Zhiyong Lu
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging.
no code implementations • 11 Jan 2024 • Tejas Sudharshan Mathai, Bohan Liu, Ronald M. Summers
Purpose: Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia.
no code implementations • 10 Jan 2024 • Benjamin Hou, Tejas Sudharshan Mathai, Jianfei Liu, Christopher Parnell, Ronald M. Summers
This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool.
no code implementations • 11 Dec 2023 • Yan Zhuang, Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee, Boah Kim, Ronald M. Summers
As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis.
no code implementations • 22 Nov 2023 • Andrew M. Nguyen, Tejas Sudharshan Mathai, Liangchen Liu, Jianfei Liu, Ronald M. Summers
In this pilot work, we developed a fully automated approach for the measurement of PCAT mean attenuation and volume in the region around both coronary arteries.
1 code implementation • 14 Jun 2023 • Qingqing Zhu, Tejas Sudharshan Mathai, Pritam Mukherjee, Yifan Peng, Ronald M. Summers, Zhiyong Lu
Pre-filling a radiology report holds promise in mitigating reporting errors, and despite efforts in the literature to generate medical reports, there exists a lack of approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset.
no code implementations • 31 Mar 2022 • Tejas Sudharshan Mathai, SungWon Lee, Thomas C. Shen, Zhiyong Lu, Ronald M. Summers
Results: Experiments on 122 test T2 MRI volumes revealed that VFNet achieved a 51. 1% mAP and 78. 7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52. 3% and sensitivity of 78. 7% at 4FP.
no code implementations • 9 Nov 2021 • Tarun Mattikalli, Tejas Sudharshan Mathai, Ronald M. Summers
In clinical practice, radiologists are reliant on the lesion size when distinguishing metastatic from non-metastatic lesions.
no code implementations • 9 Nov 2021 • Tejas Sudharshan Mathai, SungWon Lee, Daniel C. Elton, Thomas C. Shen, Yifan Peng, Zhiyong Lu, Ronald M. Summers
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases.
no code implementations • 13 Nov 2020 • Edward Chen, Tejas Sudharshan Mathai, Vinit Sarode, Howie Choset, John Galeotti
Identifying landmarks in the femoral area is crucial for ultrasound (US) -based robot-guided catheter insertion, and their presentation varies when imaged with different scanners.
no code implementations • 12 Oct 2020 • Tejas Sudharshan Mathai, Yi Wang, Nathan Cross
In this paper, we seek to quantify the bias in terms of the impact that different levels of motion artifacts have on the performance of neural networks engaged in a lesion segmentation task.
no code implementations • 7 May 2019 • Jiahong Ouyang, Tejas Sudharshan Mathai, Kira Lathrop, John Galeotti
To the best of our knowledge, this is the first approach to remove severe specular artifacts and speckle noise patterns (prior to the shallowest interface) that affects the interpretation of anterior segment OCT datasets, thereby resulting in the accurate segmentation of the shallowest tissue interface.
no code implementations • 15 Oct 2018 • Tejas Sudharshan Mathai, Kira Lathrop, John Galeotti
To the best of our knowledge, this is the first deep learning based approach to segment both anterior and posterior corneal tissue interfaces.
no code implementations • 23 Jul 2018 • Tejas Sudharshan Mathai, Lingbo Jin, Vijay Gorantla, John Galeotti
Ultra High Frequency Ultrasound (UHFUS) enables the visualization of highly deformable small and medium vessels in the hand.