Data from: An integrated iterative annotation technique for easing neural network training in medical image analysis

Neural networks promise to bring robust, quantitative analysis to medical
fields. However, their adoption is limited by the technicalities of
training these networks and the required volume and quality of
human-generated annotations. To address this gap in the field of
pathology, we have created an intuitive interface for data annotation and
the display of neural network predictions within a commonly used digital
pathology whole-slide viewer. This strategy used a ‘human-in-the-loop’ to
reduce the annotation burden. We demonstrate that segmentation of human
and mouse renal micro compartments is repeatedly improved when humans
interact with automatically generated annotations throughout the training
process. Finally, to show the adaptability of this technique to other
medical imaging fields, we demonstrate its ability to iteratively segment
human prostate glands from radiology imaging data.

神经网络有望为医学领域带来稳健、定量的分析方法。然而，其应用受到训练技术复杂度以及人工标注所需的数量与质量的限制。为填补病理学领域的这一空白，我们在常用的数字病理学全切片查看器中开发了一个直观的界面，用于数据标注和神经网络预测结果的展示。该策略采用‘人机协同（human-in-the-loop）’模式以减轻标注负担。我们证明，在训练过程中，当人类与自动生成的标注进行交互时，人类和小鼠肾脏微结构的分割效果可得到持续提升。最后，为展示该技术在其他医学影像领域的适应性，我们验证了其从放射影像数据中迭代分割人类前列腺腺体的能力。