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且量化的分析能力。然而，这类网络的应用受到其训练流程的技术复杂度，以及人工生成标注所需的规模与质量要求的限制。为填补病理学领域的这一研究空白，我们在一款通用的数字病理全切片查看器（digital pathology whole-slide viewer）中开发了一款直观的数据标注界面，用于展示神经网络的预测结果。该策略采用“人在回路（human-in-the-loop）”模式以降低标注负担。我们证实，在训练过程中让人工与自动生成的标注进行交互时，人和小鼠肾脏微区室的图像分割效果可得到持续提升。最后，为验证该技术在其他医学影像领域的适配性，我们展示了其可从放射影像数据中迭代分割人类前列腺腺体的能力。