Abstract
Clinical application of deep learning (DL)-based organ-of-interest (OOI) segmentation in radiotherapy requires case-level manual verification and adjustment. In current clinical practice, the full structure is typically verified and adjusted, limiting efficiency as not every local adjustment translates into a meaningful change in the treatment plan. Identifying such negligible adjustments could allow their omission, however no framework currently predicts this at the level of local corrections. In order to evaluate which edits matter, we introduce a framework that clusters the total discrepancy between a pair of DL and clinically used contours into spatially and directionally coherent local correction regions. We next apply each correction region individually to the original DL contour to simulate a realistic, isolated local correction. We quantify its dosimetric impact as the resulting change in mean dose under the original treatment plan compared to the original DL contour. We apply this framework to a set of 15 head and neck OOIs across 61 patients. Using this set, we train a gradient boosting classifier to distinguish negligible ( 0.01 Gy dose difference) from non-negligible local corrections (>0.01 Gy) using the following features: OOI-type, target location, original dose-level, distance to target, and correction volume. The classifier achieved an ROC-AUC of 0.93 and average precision of 0.89. This framework could be applied to inform the development of standardised, population-level review guidelines that move from full structure correction towards regional, prioritised editing. Furthermore, when applied directly to a given patient, the same approach could provide case-specific guidance during segmentation correction, for example by decomposing a full uncertainty map into candidate editing regions. This could be particularly valuable in time-critical settings such as online adaptive radiotherapy.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_016.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{vanJoë_Which_MICCAISAT2026,
author = { van Aalst, Joëlle E. AND Janssen, Tomas M. AND Maruccio, Federica AND Simões, Rita AND Fernandez Salamanca, Mar AND van Ooijen, Peter M. A. AND Brouwer, Charlotte L.},
title = { { Which Edits Matter? Simulating Realistic Local Corrections to Organ-of-Interest DL Segmentation and Predicting Dosimetric Impact } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}