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：显微图像分析中的一个基本挑战是实例分割（IS），尤其在分割集群区域时，多个不同大小和形状的物体可能在任意方向上连接甚至重叠。现有IS方法通常依赖关键点、水平边界框（h-bbox）等粗粒度实例表示，难以处理此类场景。本文提出一个名为A2B-IS的新型单阶段框架以应对这一挑战，提升显微图像中IS的准确性。我们的方法为每个实例生成像素级掩膜图和旋转边界框（r-bbox）。与使用框提议进行分割的两阶段方法不同，本方法解耦掩膜与框的预测，实现同步处理以简化模型流程。此外，我们引入高斯骨架图，从两个关键方面辅助IS任务：（1）引导锚点放置，降低计算成本，并通过滤除背景区域噪声来增强模型学习RoI感知特征的能力；（2）通过修正实例边界附近的错误框预测，确保密集分布实例的精确隔离。为进一步提升性能，我们在框架中整合两个模块：（1）设计用于提取带精细粒度多尺度信息的高分辨率特征图的空洞注意力模块（A2B），以及（2）利用有标签和无标签图像进行模型训练的半监督学习（SSL）策略。本方法已在两个大规模显微数据集上得到充分验证，证明其相较于大多数先进方法具有显著优越性。