Keshav Jha

Keshav Jha, William Sharp, Dominic LaBella

Accurate delineation of pancreatic tumors is critical for diagnosis, treatment planning, and outcome assessment, yet automated segmentation remains challenging due to anatomical variability and limited dataset availability. In this study, SegResNet models, as part of the Auto3DSeg architecture, were trained and evaluated on two MRI-based pancreatic tumor segmentation tasks as part of the 2025 PANTHER Challenge. Algorithm methodology included 5-fold cross-validation with STAPLE ensembling after focusing on an anatomically relevant region-of-interest. The Pancreatic Tumor Segmentation on Diagnostic MRI task 1 training set included 91 T1-weighted arterial contrast-enhanced MRI with expert annotated pancreas and tumor labels. The Pancreatic Tumor Segmentation on MR-Linac task 2 training set used 50 T2-weighted MR-Linac cases with expert annotated pancreas and tumor labels. Algorithm-automated segmentation performance of pancreatic tumor was assessed using Dice Similarity Coefficient (DSC), 5 mm DSC, 95th percentile Hausdorff Distance (HD95), Mean Average Surface Distance (MASD), and Root Mean Square Error (RMSE). For Task 1, the algorithm achieved a DSC of 0.56, 5 mm DSC of 0.73, HD95 of 41.1 mm, MASD of 26.0 mm, and RMSE of 5164 mm. For Task 2, performance decreased, with a DSC of 0.33, 5 mm DSC of 0.50, HD95 of 20.1 mm, MASD of 7.2 mm, and RMSE of 17,203 mm. These findings illustrate the challenges of MRI-based pancreatic tumor segmentation with small datasets, highlighting variability introduced by different MRI sequences. Despite modest performance, the results demonstrate potential for automated delineation and emphasize the need for larger, standardized MRI datasets to improve model robustness and clinical utility.

Dominic LaBella, Keshav Jha, Jared Robbins et al.

Cone-beam computed tomography (CBCT) has become an invaluable imaging modality in dentistry, enabling 3D visualization of teeth and surrounding structures for diagnosis and treatment planning. Automated segmentation of dental structures in CBCT can efficiently assist in identifying pathology (e.g., pulpal or periapical lesions) and facilitate radiation therapy planning in head and neck cancer patients. We describe the DLaBella29 team's approach for the MICCAI 2025 ToothFairy3 Challenge, which involves a deep learning pipeline for multi-class tooth segmentation. We utilized the MONAI Auto3DSeg framework with a 3D SegResNet architecture, trained on a subset of the ToothFairy3 dataset (63 CBCT scans) with 5-fold cross-validation. Key preprocessing steps included image resampling to 0.6 mm isotropic resolution and intensity clipping. We applied an ensemble fusion using Multi-Label STAPLE on the 5-fold predictions to infer a Phase 1 segmentation and then conducted tight cropping around the easily segmented Phase 1 mandible to perform Phase 2 segmentation on the smaller nerve structures. Our method achieved an average Dice of 0.87 on the ToothFairy3 challenge out-of-sample validation set. This paper details the clinical context, data preparation, model development, results of our approach, and discusses the relevance of automated dental segmentation for improving patient care in radiation oncology.