arXiv subjects

Vamsi Thiriveedhi

Publications and source records attributed to Vamsi Thiriveedhi.

2 recordsLinked to original sources

arXiv2026

Annotating anatomy and pathology in the National Lung Screening Trial computed tomography images

Large-scale public medical imaging datasets contribute critically to translational research. When accompanied by rich clinical and multi-omics data, they can stimulate exploratory research and enable secondary analyses. Expert annotations of such imaging collections can support the development of new image analysis tools. Continuous enrichment of images with image-derived data makes them more usable for researchers without expertise in image analysis or access to large-scale computational resources. The National Lung Screening Trial (NLST) released a rich longitudinal dataset that includes Computed Tomography (CT) images for over 26,000 patients. We introduce three Digital Imaging and Communications in Medicine (DICOM) formatted datasets, complementing NLST CT images, shared as analysis results in the National Cancer Institute Imaging Data Commons (IDC). Two of those (IDC NLSTSeg and IDC NLSTSybil) contain DICOM-harmonized annotations and extracted measurements (for 581 and 601 NLST patients, respectively) shared earlier using research formats (Sybil and NLSTseg). The third one (TotalSegmentator-CT-Segmentations) contains volumetric segmentations generated using TotalSegmentator and radiomics features for each segment for 26,194 NLST patients.

eess.IV↗

arXiv2023

Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and thus can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections of computed tomography images of the chest, NSCLC-Radiomics, and the National Lung Screening Trial. Using publicly available AI algorithms we derived volumetric annotations of thoracic organs at risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can be used to aid in cancer imaging.

cs.CV↗