Centrala begrepp
The core message of this article is to propose a strategic framework for estimating the amount of annotated data required to train satisfactory patch-based segmentation networks. This framework includes the establishment of performance expectations using a Minor Boundary Adjustment for Threshold (MinBAT) method, and standardizing patch selection through the ROI-based Expanded Patch Selection (REPS) method.
Sammanfattning
The article focuses on the early stage of deep learning research, prior to model development, and proposes a framework to estimate the amount of annotated data required for patch-based brain MRI segmentation tasks.
Key highlights:
Established performance expectations using a novel Minor Boundary Adjustment for Threshold (MinBAT) method, which determines acceptable Dice Similarity Coefficient (DSC) scores based on the size and shape of the regions of interest (ROIs).
Introduced the ROI-based Expanded Patch Selection (REPS) strategy to standardize the contribution of each training case to the model, while maintaining model performance.
Conducted experiments on three brain-related segmentation tasks (brain extraction, tumor segmentation, and MS lesion segmentation) with different ROI sizes and shapes.
Visualized learning curves to estimate and predict the required number of cases based on the target DSC scores determined by MinBAT.
Discovered relationships between the number of cases, the number of ROIs, and the achievable performance, which can assist in the design of experiments and evaluation of algorithms.
Statistik
The article does not contain any explicit numerical data or statistics. The key insights are derived from the experimental results and the proposed methods.
Citat
The article does not contain any direct quotes that are critical to the key arguments.