Optimizing the Procedure of CT Segmentation Labeling
This work addresses the cost and efficiency of data annotation for medical imaging segmentation, offering a practical, incremental improvement for researchers and practitioners in the field.
The paper tackles the problem of high labeling costs for CT segmentation datasets by analyzing how label quality, diversity, and completeness affect model performance, finding that quality is most important early on, followed by diversity, and proposes an optimized labeling procedure to minimize effort while maximizing performance.
In Computed Tomography, machine learning is often used for automated data processing. However, increasing model complexity is accompanied by increasingly large volume datasets, which in turn increases the cost of model training. Unlike most work that mitigates this by advancing model architectures and training algorithms, we consider the annotation procedure and its effect on the model performance. We assume three main virtues of a good dataset collected for a model training to be label quality, diversity, and completeness. We compare the effects of those virtues on the model performance using open medical CT datasets and conclude, that quality is more important than diversity early during labeling; the diversity, in turn, is more important than completeness. Based on this conclusion and additional experiments, we propose a labeling procedure for the segmentation of tomographic images to minimize efforts spent on labeling while maximizing the model performance.