Wodzinski Marek, Müller Henning
AGH University of Science and Technology Department of Measurement and Electronics Kraków, Poland.
University of Applied Sciences Western Switzerland (HES-SO Valais) Information Systems Institute Sierre, Switzerland.
Comput Methods Programs Biomed. 2021 Jan;198:105799. doi: 10.1016/j.cmpb.2020.105799. Epub 2020 Oct 24.
The use of several stains during histology sample preparation can be useful for fusing complementary information about different tissue structures. It reveals distinct tissue properties that combined may be useful for grading, classification, or 3-D reconstruction. Nevertheless, since the slide preparation is different for each stain and the procedure uses consecutive slices, the tissue undergoes complex and possibly large deformations. Therefore, a nonrigid registration is required before further processing. The nonrigid registration of differently stained histology images is a challenging task because: (i) the registration must be fully automatic, (ii) the histology images are extremely high-resolution, (iii) the registration should be as fast as possible, (iv) there are significant differences in the tissue appearance, and (v) there are not many unique features due to a repetitive texture.
In this article, we propose a deep learning-based solution to the histology registration. We describe a registration framework dedicated to high-resolution histology images that can perform the registration in real-time. The framework consists of an automatic background segmentation, iterative initial rotation search and learning-based affine/nonrigid registration.
We evaluate our approach using an open dataset provided for the Automatic Non-rigid Histological Image Registration (ANHIR) challenge organized jointly with the IEEE ISBI 2019 conference. We compare our solution to the challenge participants using a server-side evaluation tool provided by the challenge organizers. Following the challenge evaluation criteria, we use the target registration error (TRE) as the evaluation metric. Our algorithm provides registration accuracy close to the best scoring teams (median rTRE 0.19% of the image diagonal) while being significantly faster (the average registration time is about 2 seconds).
The proposed framework provides results, in terms of the TRE, comparable to the best-performing state-of-the-art methods. However, it is significantly faster, thus potentially more useful in clinical practice where a large number of histology images are being processed. The proposed method is of particular interest to researchers requiring an accurate, real-time, nonrigid registration of high-resolution histology images for whom the processing time of traditional, iterative methods in unacceptable. We provide free access to the software implementation of the method, including training and inference code, as well as pretrained models. Since the ANHIR dataset is open, this makes the results fully and easily reproducible.
在组织学样本制备过程中使用多种染色剂，有助于融合关于不同组织结构的互补信息。它能揭示不同的组织特性，综合起来可能有助于分级、分类或三维重建。然而，由于每种染色剂的玻片制备方法不同，且该过程使用连续切片，组织会经历复杂且可能较大的变形。因此，在进一步处理之前需要进行非刚性配准。对不同染色的组织学图像进行非刚性配准是一项具有挑战性的任务，原因如下：（i）配准必须完全自动化；（ii）组织学图像分辨率极高；（iii）配准应尽可能快；（iv）组织外观存在显著差异；（v）由于纹理重复，独特特征不多。
在本文中，我们提出了一种基于深度学习的组织学配准解决方案。我们描述了一个专门用于高分辨率组织学图像的配准框架，该框架可以实时执行配准。该框架由自动背景分割、迭代初始旋转搜索和基于学习的仿射/非刚性配准组成。
我们使用与2019年IEEE ISBI会议联合举办的自动非刚性组织学图像配准（ANHIR）挑战赛提供的开放数据集对我们的方法进行评估。我们使用挑战赛组织者提供的服务器端评估工具，将我们的解决方案与挑战赛参与者的方案进行比较。按照挑战赛评估标准，我们使用目标配准误差（TRE）作为评估指标。我们的算法提供的配准精度接近得分最高的团队（平均相对目标配准误差为图像对角线的0.19%），同时速度明显更快（平均配准时间约为2秒）。
就TRE而言，所提出的框架提供的结果与性能最佳的现有方法相当。然而，它速度明显更快，因此在处理大量组织学图像的临床实践中可能更有用。对于需要对高分辨率组织学图像进行准确、实时、非刚性配准的研究人员来说，传统迭代方法的处理时间不可接受，所提出的方法尤其有意义。我们提供该方法的软件实现的免费访问，包括训练和推理代码以及预训练模型。由于ANHIR数据集是开放的，这使得结果能够完全且容易地重现。
