Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets Conference Paper
| Authors: | Rangnekar, A.; Nadkarni, N.; Jiang, J.; Veeraraghavan, H. |
| Title: | Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets |
| Conference Title: | Medical Imaging 2025: Image Perception, Observer Performance, and Technology Assessment |
| Abstract: | Medical image foundation models have shown the ability to segment organs and tumors with minimal fine-tuning. These models are typically evaluated on task-specific in-distribution (ID) datasets. However, reliable performance on ID datasets does not guarantee robust generalization on out-of-distribution (OOD) datasets. Importantly, once deployed for clinical use, it is impractical to have ‘ground truth’ delineations to assess ongoing performance drifts, especially when images fall into the OOD category due to different imaging protocols. Hence, we introduced a comprehensive set of computationally fast metrics to evaluate the performance of multiple foundation models (Swin UNETR, SimMIM, iBOT, SMIT) trained with self-supervised learning (SSL). All models were fine-tuned on identical datasets for lung tumor segmentation from computed tomography (CT) scans. SimMIM, iBOT, and SMIT used identical architecture, pretraining, and fine-tuning datasets to assess performance variations with the choice of pretext tasks used in SSL. The evaluation was performed on two public lung cancer datasets (LRAD: n = 140, 5Rater: n = 21) with different image acquisitions and tumor stages compared to training data (n = 317 public resource with stage III-IV lung cancers) and a public non-cancer dataset containing volumetric CT scans of patients with pulmonary embolism (n = 120). All models produced similarly accurate tumor segmentation on the lung cancer testing datasets. SMIT produced the highest F1-score (LRAD: 0.60, 5Rater: 0.64) and lowest entropy (LRAD: 0.06, 5Rater: 0.12), indicating higher tumor detection rate and confident segmentations. In the OOD dataset, SMIT misdetected the least number of tumors, marked by a median volume occupancy of 5.67 cc compared to the best method SimMIM of 9.97 cc. Our analysis shows that additional metrics such as entropy and volume occupancy may help better understand model performance on mixed domain datasets. © 2025 SPIE. |
| Keywords: | lung cancer; segmentation; computed tomography; fine tuning; image segmentation; pulmonary diseases; tumor segmentation; computed tomography scan; foundation models; in-distribution; out-of-distribution; mixed domains |
| Journal Title | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
| Volume: | 13409 |
| Conference Dates: | 2025 Feb 16-19 |
| Conference Location: | San Deigo, CA |
| ISBN: | 1605-7422 |
| Publisher: | SPIE |
| Date Published: | 2025-01-01 |
| Start Page: | 1340914 |
| Language: | English |
| DOI: | 10.1117/12.3047709 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Conference paper (ISBN 9781510685963) -- Source: Scopus |
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159Veeraraghavan -
79Jiang -
11Rangnekar -
2
Nadkarni
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