Adversarial Multiscale Feature Learning Framework for Overlapping Chromosome Segmentation

ChromTR: chromosome detection in raw metaphase cell images via deformable transformers

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.

An Integral R-Banded Karyotype Analysis System of Bone Marrow Metaphases Based on Deep Learning

CONTEXT.—

Conventional karyotype analysis, which provides comprehensive cytogenetic information, plays a significant role in the diagnosis and risk stratification of hematologic neoplasms. The main limitations of this approach include long turnaround time and laboriousness. Therefore, we developed an integral R-banded karyotype analysis system for bone marrow metaphases, based on deep learning.

OBJECTIVE.—

To evaluate the performance of the internal models and the entire karyotype analysis system for R-banded bone marrow metaphase.

DESIGN.—

A total of 4442 sets of R-banded normal bone marrow metaphases and karyograms were collected. Accordingly, 4 deep learning-based models for different analytic stages of karyotyping, including denoising, segmentation, classification, and polarity recognition, were developed and integrated as an R-banded bone marrow karyotype analysis system. Five-fold cross validation was performed on each model. The whole system was implemented by 2 strategies of automatic and semiautomatic workflows. A test set of 885 metaphases was used to assess the entire system.

RESULTS.—

The denoising model achieved an intersection-over-union (IoU) of 99.20% and a Dice similarity coefficient (DSC) of 99.58% for metaphase acquisition. The segmentation model achieved an IoU of 91.95% and a DSC of 95.79% for chromosome segmentation. The accuracies of the segmentation, classification, and polarity recognition models were 96.77%, 98.77%, and 99.93%, respectively. The whole system achieved an accuracy of 93.33% with the automatic strategy and an accuracy of 99.06% with the semiautomatic strategy.

CONCLUSIONS.—

The performance of both the internal models and the entire system is desirable. This deep learning-based karyotype analysis system has potential in a clinical application.

KaryoXpert: An accurate chromosome segmentation and classification framework for karyotyping analysis without training with manually labeled metaphase-image mask annotations

Automated karyotyping is of great importance for cytogenetic research, as it speeds up the process for cytogeneticists through incorporating AI-driven automated segmentation and classification techniques. Existing frameworks confront two primary issues: Firstly the necessity for instance-level data annotation with either detection bounding boxes or semantic masks for training, and secondly, its poor robustness particularly when confronted with domain shifts. In this work, we first propose an accurate segmentation framework, namely KaryoXpert. This framework leverages the strengths of both morphology algorithms and deep learning models, allowing for efficient training that breaks the limit for the acquirement of manually labeled ground-truth mask annotations. Additionally, we present an accurate classification model based on metric learning, designed to overcome the challenges posed by inter-class similarity and batch effects. Our framework exhibits state-of-the-art performance with exceptional robustness in both chromosome segmentation and classification. The proposed KaryoXpert framework showcases its capacity for instance-level chromosome segmentation even in the absence of annotated data, offering novel insights into the research for automated chromosome segmentation. The proposed method has been successfully deployed to support clinical karyotype diagnosis.

Global Semantic-Sense Aggregation Network for Salient Object Detection in Remote Sensing Images

Salient object detection (SOD) aims to accurately identify significant geographical objects in remote sensing images (RSI), providing reliable support and guidance for extensive geographical information analyses and decisions. However, SOD in RSI faces numerous challenges, including shadow interference, inter-class feature confusion, as well as unclear target edge contours. Therefore, we designed an effective Global Semantic-aware Aggregation Network (GSANet) to aggregate salient information in RSI. GSANet computes the information entropy of different regions, prioritizing areas with high information entropy as potential target regions, thereby achieving precise localization and semantic understanding of salient objects in remote sensing imagery. Specifically, we proposed a Semantic Detail Embedding Module (SDEM), which explores the potential connections among multi-level features, adaptively fusing shallow texture details with deep semantic features, efficiently aggregating the information entropy of salient regions, enhancing information content of salient targets. Additionally, we proposed a Semantic Perception Fusion Module (SPFM) to analyze map relationships between contextual information and local details, enhancing the perceptual capability for salient objects while suppressing irrelevant information entropy, thereby addressing the semantic dilution issue of salient objects during the up-sampling process. The experimental results on two publicly available datasets, ORSSD and EORSSD, demonstrated the outstanding performance of our method. The method achieved 93.91% Sα, 98.36% Eξ, and 89.37% Fβ on the EORSSD dataset.