  Training deep neural networks reliably requires access to large-scale
datasets. However, obtaining such datasets can be challenging, especially in
the context of neuroimaging analysis tasks, where the cost associated with
image acquisition and annotation can be prohibitive. To mitigate both the time
and financial costs associated with model development, a clear understanding of
the amount of data required to train a satisfactory model is crucial. This
paper focuses on an early stage phase of deep learning research, prior to model
development, and proposes a strategic framework for estimating the amount of
annotated data required to train patch-based segmentation networks. This
framework includes the establishment of performance expectations using a novel
Minor Boundary Adjustment for Threshold (MinBAT) method, and standardizing
patch selection through the ROI-based Expanded Patch Selection (REPS) method.
Our experiments demonstrate that tasks involving regions of interest (ROIs)
with different sizes or shapes may yield variably acceptable Dice Similarity
Coefficient (DSC) scores. By setting an acceptable DSC as the target, the
required amount of training data can be estimated and even predicted as data
accumulates. This approach could assist researchers and engineers in estimating
the cost associated with data collection and annotation when defining a new
segmentation task based on deep neural networks, ultimately contributing to
their efficient translation to real-world applications.
