IEEE Trans Med Imaging. 2021 Jul;40(7):1924-1933. doi: 10.1109/TMI.2021.3069154. Epub 2021 Jun 30.
There has been a long pursuit for precise and reproducible glomerular quantification on renal pathology to leverage both research and practice. When digitizing the biopsy tissue samples using whole slide imaging (WSI), a set of serial sections from the same tissue can be acquired as a stack of images, similar to frames in a video. In radiology, the stack of images (e.g., computed tomography) are naturally used to provide 3D context for organs, tissues, and tumors. In pathology, it is appealing to do a similar 3D assessment. However, the 3D identification and association of large-scale glomeruli on renal pathology is challenging due to large tissue deformation, missing tissues, and artifacts from WSI. In this paper, we propose a novel Multi-object Association for Pathology in 3D (Map3D) method for automatically identifying and associating large-scale cross-sections of 3D objects from routine serial sectioning and WSI. The innovations of the Multi-Object Association for Pathology in 3D (Map3D) method are three-fold: (1) the large-scale glomerular association is formed as a new multi-object tracking (MOT) perspective; (2) the quality-aware whole series registration is proposed to not only provide affinity estimation but also offer automatic kidney-wise quality assurance (QA) for registration; (3) a dual-path association method is proposed to tackle the large deformation, missing tissues, and artifacts during tracking. To the best of our knowledge, the Map3D method is the first approach that enables automatic and large-scale glomerular association across 3D serial sectioning using WSI. Our proposed method Map3D achieved MOTA = 44.6, which is 12.1% higher than the non-deep learning benchmarks.
长期以来，人们一直致力于在肾脏病理学中实现精确且可重现的肾小球定量，以便在研究和实践中均能充分利用。在使用全切片成像（WSI）对活检组织样本进行数字化时，可以获取同一组织的一组连续切片作为图像堆栈，类似于视频中的帧。在放射学中，图像堆栈（例如 CT）自然用于为器官、组织和肿瘤提供 3D 上下文。在病理学中，进行类似的 3D 评估很有吸引力。然而，由于组织变形大、组织缺失和 WSI 产生的伪影，对肾脏病理学中的大规模肾小球进行 3D 识别和关联具有挑战性。在本文中，我们提出了一种新颖的用于 3D 病理学的多目标关联（Map3D）方法，用于自动识别和关联来自常规连续切片和 WSI 的大规模 3D 对象的横截面。多目标关联用于 3D 病理学（Map3D）方法的创新之处有三点：（1）将大规模肾小球关联形成新的多目标跟踪（MOT）视角；（2）提出了基于质量感知的全系列配准方法，不仅提供亲和力估计，还为配准提供自动肾脏质量保证（QA）；（3）提出了一种双路径关联方法，以解决跟踪过程中的大变形、组织缺失和伪影问题。据我们所知，Map3D 方法是第一个能够使用 WSI 实现 3D 连续切片自动大规模肾小球关联的方法。我们提出的方法 Map3D 实现了 MOTA=44.6，比非深度学习基准高 12.1%。
