## 1 Introduction  
The advancement of biology increasingly depends on innovations in in vitro cell culture technologies. Traditional two-dimensional (2D) cell culture models lack tissue-specific architecture and fail to accurately replicate complex physiological environments [1]. In contrast, organoids--self-organized, three-dimensional (3D) cultures--can mimic the cellular composition, structural organization, biological properties, and key functions of native tissues. As such, they serve as powerful platforms for disease modeling, drug screening, and precision medicine [2]. To monitor organoid development, researchers routinely employ fluorescence microscopy for the periodic assessment of morphology and growth dynamics [3]. Common analytical tasks include cell enumeration, quantification of region-specific biomarker intensities, and morphometric analysis (e.g., size, shape) [4]. However, manual quantification remains labor-intensive: a single experiment may require processing hundreds of microscopy images, each containing dozens to hundreds of organoids [5]. This bottleneck limits the scalability of high-throughput organoid technologies. To address this challenge, [PERSON] et al. [6] introduced OrgaoSeg, an open-source tool for automated image quantification. However, its reliance on classical image processing methods--such as threshold segmentation and morphological filtering--restricts its adaptability across diverse imaging conditions. These limitations underscore the growing need for deep learning-based approaches that offer superior generalization and cross-scenario robustness. The convergence of artificial intelligence (AI) and bioinformatics is catalyzing a paradigm shift in organoid analysis, enabling scalable, high-throughput pipelines that enhance both biological discovery and translational applications [7]. Among AI methodologies, object detection [8] has emerged as a critical tool, combining classification with spatial localization to support quantitative organoid counting, spatial distribution analysis, and heterogeneity characterization. Within this domain, the YOLO (You Only Look Once) series [9] is known for its real-time detection speed. However, its anchor-based architecture exhibits limited sensitivity to small and overlapping organoid structures, leading to frequent missed detections. Conversely, Faster R-CNN [10] improves detection accuracy through its region proposal network (RPN), but suffers from computational inefficiencies when applied to high-resolution microscopy images [11]. Several organoid-specific detection models have recently demonstrated promising results. [PERSON] et al. [12] developed OrgaQuant, a CNN-based tool for detecting and quantifying intestinal organoids in bright-field microscopy images. It achieved a mean average precision at an intersection over union (IoU) threshold of 0.5 ([EMAIL_ADDRESS]) of 80%, substantially increasing analytical throughput. Similarly, [PERSON] et al. [13] introduced Deep-LUMEN, which detects morphological transitions in lung epithelial spheroids and distinguishes luminal from non-luminal structures in bright-field images, attaining a [EMAIL_ADDRESS] of 73% without data augmentation. This offers a novel framework for respiratory organoid analysis. [PERSON] et al. [14] applied ResNet50 to develop a three-stage classification system for retinal organoids, achieving an [EMAIL_ADDRESS] of 84% by leveraging early-stage morphological features for fate prediction--outperforming expert manual evaluations. [PERSON] et al. [15] employed YOLO to build an automated detection system for induced pluripotent stem cell (iPSC) colonies, reaching a [EMAIL_ADDRESS] of 89.8%, thus demonstrating strong reliability and surpassing manual quantification in consistency. Addressing the complexity of cellular morphology, [PERSON] et al. [16] incorporated a feature pyramid network (FPN) into the Faster R-CNN framework to enhance the detection of abnormal cervical cells in cytology images, resulting in a 6-9% [EMAIL_ADDRESS] improvement over traditional methods.