Accurate segmentation of coronary arteries is a pivotal process in assessing
cardiovascular diseases. However, the intricate structure of the cardiovascular
system presents significant challenges for automatic segmentation, especially
when utilizing methodologies like the SYNTAX Score, which relies extensively on
detailed structural information for precise risk stratification. To address
these difficulties and cater to this need, we present MPSeg, an innovative
multi-phase strategy designed for coronary artery segmentation. Our approach
specifically accommodates these structural complexities and adheres to the
principles of the SYNTAX Score. Initially, our method segregates vessels into
two categories based on their unique morphological characteristics: Left
Coronary Artery (LCA) and Right Coronary Artery (RCA). Specialized ensemble
models are then deployed for each category to execute the challenging
segmentation task. Due to LCA's higher complexity over RCA, a refinement model
is utilized to scrutinize and correct initial class predictions on segmented
areas. Notably, our approach demonstrated exceptional effectiveness when
evaluated in the Automatic Region-based Coronary Artery Disease diagnostics
using x-ray angiography imagEs (ARCADE) Segmentation Detection Algorithm
challenge at MICCAI 2023.

我们介绍了一种名为 MPSeg 的创新多阶段策略，专门用于冠状动脉分割，其采用了分离左冠状动脉和右冠状动脉以及集成模型等方法，针对冠状动脉的结构复杂性和 SYNTAX Score 的原则进行了设计和优化，并在 MICCAI 2023 年的 ARCADE 挑战中表现出了卓越的效果。

MPSeg：冠状动脉分割的多阶段策略

MPSeg : Multi-Phase strategy for coronary artery Segmentation

Podocytes, specialized epithelial cells that envelop the glomerular
capillaries, play a pivotal role in maintaining renal health. The current
description and quantification of features on pathology slides are limited,
prompting the need for innovative solutions to comprehensively assess diverse
phenotypic attributes within Whole Slide Images (WSIs). In particular,
understanding the morphological characteristics of podocytes, terminally
differentiated glomerular epithelial cells, is crucial for studying glomerular
injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies
it to podocyte pathomics. The SPT consists of three main components: (1)
instance object segmentation, enabling precise identification of podocyte
nuclei; (2) pathomics feature generation, extracting a comprehensive array of
quantitative features from the identified nuclei; and (3) robust statistical
analyses, facilitating a comprehensive exploration of spatial relationships
between morphological and spatial transcriptomics features.The SPT successfully
extracted and analyzed morphological and textural features from podocyte
nuclei, revealing a multitude of podocyte morphomic features through
statistical analysis. Additionally, we demonstrated the SPT's ability to
unravel spatial information inherent to podocyte distribution, shedding light
on spatial patterns associated with glomerular injury. By disseminating the
SPT, our goal is to provide the research community with a powerful and
user-friendly resource that advances cellular spatial pathomics in renal
pathology. The implementation and its complete source code of the toolkit are
made openly accessible at this https URL

本文介绍了空间病理学工具包（SPT）并应用于肾小球细胞病理学的研究，成功从肾小球细胞核中提取并分析了形态学和纹理特征，揭示了肾小球形态学特征和空间分布模式与肾小球损伤相关的信息。

肾脏病理学中利用组织学和空间转录组学数据对足细胞核进行定量分析的空间病理学工具包

Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei  with Histology and Spatial Transcriptomics Data in Renal Pathology

As the use of artificial intelligent (AI) models becomes more prevalent in
industries such as engineering and manufacturing, it is essential that these
models provide transparent reasoning behind their predictions. This paper
proposes the AI-Reasoner, which extracts the morphological characteristics of
defects (DefChars) from images and utilises decision trees to reason with the
DefChar values. Thereafter, the AI-Reasoner exports visualisations (i.e.
charts) and textual explanations to provide insights into outputs made by
masked-based defect detection and classification models. It also provides
effective mitigation strategies to enhance data pre-processing and overall
model performance. The AI-Reasoner was tested on explaining the outputs of an
IE Mask R-CNN model using a set of 366 images containing defects. The results
demonstrated its effectiveness in explaining the IE Mask R-CNN model's
predictions. Overall, the proposed AI-Reasoner provides a solution for
improving the performance of AI models in industrial applications that require
defect analysis.

提出了 AI 推理器，它从图像中提取缺陷的形态特征（DefChars），并利用决策树对 DefChar 值进行推理，随后导出可视化和文字解释以提供对基于掩膜的缺陷检测和分类模型输出的洞察，并为增强数据预处理和整体模型性能提供有效的缓解策略。通过对包含缺陷的 366 张图像的 IE Mask R-CNN 模型输出进行解释，测试了 AI 推理器的有效性，结果展示了它在解释 IE Mask R-CNN 模型的预测方面的有效性。总体而言，提出的 AI 推理器为需要缺陷分析的工业应用中改善 AI 模型的性能提供了解决方案。