Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases, of which chromosome detection in raw metaphase cell images is the most critical and challenging step. In this work, focusing on the joint optimization of chromosome localization and classification, we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell images. ChromTR incorporates semantic feature learning and class distribution learning into a unified DETR-based detection framework. Specifically, we first propose a Semantic Feature Learning Network (SFLN) for semantic feature extraction and chromosome foreground region segmentation with object-wise supervision. Next, we construct a Semantic-Aware Transformer (SAT) with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic features. To provide a prediction with a precise chromosome number and category distribution, a Category Distribution Reasoning Module (CDRM) is built for foreground-background objects and chromosome class distribution reasoning. We evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset AutoKary2022. Our proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56% in R-band chromosome detection, surpassing the baseline method by 3.02%. In a clinical test, ChromTR is also confident in tackling normal and numerically abnormal karyotypes. When extended to the chromosome enumeration task, ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image datasets. Given these superior performances to other methods, our proposed method has been applied to assist clinical karyotype diagnosis.
Humans ; Metaphase ; Karyotyping/methods* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Chromosomes, Human/genetics*

The construction of medical knowledge platform is a core value of the intelligent construction of electronic medical records. The hospital-wide knowledge base construction covers a wide range of content, including multiple healthcare scenarios such as medicine, testing, inspection, surgery, blood transfusion and nursing. This article introduced how Jiangsu Province People′s Hospital used knowledge graphs and rule engine to construct a hospital knowledge management platform, realize the integration of knowledge-based knowledge base and a non-knowledge-based knowledge base, and embed clinical diagnosis and treatment rules into the information system for different flexible application scenarios.Finally, a multi-dimensional knowledge base was formed to realize the unified knowledge information integration of various clinical expert knowledge, and to provide integrated display and decision support for all departments, as well as realizing real-time data verification, prompting and control in each link.