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.

本文提出了一种应用于深度学习的早期阶段研究的策略性框架，用于估计训练基于补丁的分割网络所需的注释数据量。通过使用一种新颖的边界微调方法（MinBAT）建立性能预期，并通过基于感兴趣区域（ROI）的扩展补丁选择方法（REPS）标准化补丁选择，我们的实验表明，涉及不同大小或形状的感兴趣区域（ROIs）的任务可能会产生可接受的 Dice 相似系数（DSC）分数变化。通过将可接受的 DSC 设为目标，可以估计所需的训练数据量，甚至可以随着数据的积累进行预测。这种方法可帮助研究人员和工程师估计定义基于深度神经网络的新分割任务时的数据收集和注释成本，最终有助于它们有效地应用于实际应用。