Umbreen Neelam, Ali Sara, Sajid Hasan, Ayaz Yasar, Alsenan Shrooq, Nam Yunyoung, Kim So Yeon, Sial Muhammad Baber
National University of Sciences and Technology (NUST), Islamabad, Pakistan.
National University of Sciences and Technology (NUST), Islamabad, Pakistan; Suzhou Industrial Park (SIP) Monash Research Institute of Science and Technology, Monash University, 8Q9C+R6W, Suzhou, Jiangsu, China.; School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad, NUST-COVENTRY Human-Robot Interaction (NC-HRI) Laboratory, Pakistan; Intelligent Field Robotics Laboratory (IFRL), National Center for Artificial Intelligence (NCAI), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
SLAS Technol. 2025 May 11:100306. doi: 10.1016/j.slast.2025.100306.
Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imaging.
由于中期图像中染色体分割存在内在复杂性、染色体形状的变异性以及高质量标注数据集的稀缺性，因此在细胞遗传学和基因组学中，中期图像的染色体分割是一项关键但具有挑战性的任务。本研究提出了一种强大的实例分割框架，该框架将自动标注管道与增强的深度学习架构相结合，以应对这些挑战。引入了一个新颖的数据集，该数据集由中期图像和相应的核型图组成，并以COCO格式跨24个染色体类别标注了精确的实例分割信息。为了克服劳动强度大的手动标注过程，采用了一种基于特征的图像配准技术，该技术利用尺度不变特征变换（SIFT）和单应性，能够将核型图中的染色体准确映射到中期图像，显著提高标注质量和分割性能。所提出的框架包括一个定制的掩码区域卷积神经网络（Mask R-CNN）模型，该模型通过基于注意力的特征金字塔网络（AttFPN）、空间注意力机制和一个LastLevelMaxPool模块进行增强，以实现卓越的多尺度特征提取，并将注意力集中在图像的关键区域。实验评估证明了该模型的有效性，在交并比（IoU）=0.50:0.95时，平均精度均值（mAP）达到0.579，在mAP和AP50方面分别比基线Mask R-CNN和带有AttFPN的Mask R-CNN提高了3.94%和5.97%。值得注意的是，所提出的架构在分割中小型染色体方面表现出色，解决了现有方法的关键局限性。本研究不仅引入了一个先进的分割框架，还提供了一个基准数据集，为生物医学成像中的染色体实例分割设定了新的标准。自动数据集创建与先进模型设计的集成提供了一个可扩展且可转移的解决方案，为解决生物医学和细胞遗传学成像其他领域的类似挑战铺平了道路。
