Many Raters, One Mask? A Practical Guide to Consensus Aggregation for Multi-Annotator Medical Image Segmentation
Note. This tutorial's interactive widgets and its citation previews require JavaScript to be enabled in your browser. If you were to ask seven radiologists to outline the same structure on the same medical image, it is almost a certainty that you will get seven different masks. To someone unfamiliar with medical imaging, this might seem like carelessness, but it is not. Boundaries in medical images are genuinely ambiguous, and if you add personal annotation preferences or “style”, expertise levels, and other factors, the disagreement starts to make sense. Across organs and modalities, the volume of a manually contoured structure routinely varies by tens of percent between observers (for example, inter-observer volume differences of 20% and more are common in CT organ delineation), and that variation propagates directly into downstream tasks: radiotherapy decisions, measurements, and the “ground truth” on which segmentation models are trained and evaluated. When these annotations feed a supervised model, the choice of how to collapse them into a single training target is a modeling decision, and this tutorial shows how different methods can make that decision differently. ...