One of the fundamental challenges in microscopy (MS) image analysis is instance segmentation (IS), particularly when segmenting cluster regions where multiple objects of varying sizes and shapes may be connected or even overlapped in arbitrary orientations. Existing IS methods usually fail in handling such scenarios, as they rely on coarse instance representations such as keypoints and horizontal bounding boxes (h-bboxes). In this paper, we propose a novel one-stage framework named A2B-IS to address this challenge and enhance the accuracy of IS in MS images. Our approach represents each instance with a pixel-level mask map and a rotated bounding box (r-bbox). Unlike two-stage methods that use box proposals for segmentations, our method decouples mask and box predictions, enabling simultaneous processing to streamline the model pipeline. Additionally, we introduce a Gaussian skeleton map to aid the IS task in two key ways: (1) It guides anchor placement, reducing computational costs while improving the model's capacity to learn RoI-aware features by filtering out noise from background regions. (2) It ensures accurate isolation of densely packed instances by rectifying erroneous box predictions near instance boundaries. To further enhance the performance, we integrate two modules into the framework: (1) An Atrous Attention Block (A2B) designed to extract high-resolution feature maps with fine-grained multiscale information, and (2) A Semi-Supervised Learning (SSL) strategy that leverages both labeled and unlabeled images for model training. Our method has been thoroughly validated on two large-scale MS datasets, demonstrating its superiority over most state-of-the-art approaches.

翻译：显微图像分析中的核心挑战之一是实例分割，尤其当分割包含多个大小形态各异、可能以任意方向连接甚至重叠的聚类区域时，现有实例分割方法通常难以处理此类场景，这源于其依赖关键点、水平边界框等粗粒度实例表征。本文提出一种名为A2B-IS的新型单阶段框架以应对该挑战，提升显微图像实例分割精度。该方法采用像素级掩膜图与旋转边界框作为每个实例的表征。不同于利用框提议进行分割的两阶段方法，本方法将掩膜与边界框预测解耦，实现同步处理以简化模型流程。此外，我们引入高斯骨架图通过两种关键方式辅助实例分割任务：(1) 指导锚点配置，通过过滤背景区域噪声降低计算成本，同时提升模型学习感兴趣区域感知特征的能力；(2) 通过修正实例边界附近的错误框预测，确保密集堆积实例的精确分离。为进一步提升性能，我们在框架中集成两个模块：(1) 空洞注意力模块，用于提取包含细粒度多尺度信息的高分辨率特征图；(2) 半监督学习策略，利用标记与未标记图像共同训练模型。该方法在两个大规模显微图像数据集上经过充分验证，展现出优于多数最新方法的性能。