Boundary detection of irregular and translucent objects is an important problem with applications in medical imaging, environmental monitoring and manufacturing, where many of these applications are plagued with scarce labeled data and low in situ computational resources. While recent image segmentation studies focus on segmentation mask alignment with ground-truth, the task of boundary detection remains understudied, especially in the low data regime. In this work, we present a lightweight discrete diffusion contour refinement pipeline for robust boundary detection in the low data regime. We use a Convolutional Neural Network(CNN) architecture with self-attention layers as the core of our pipeline, and condition on a segmentation mask, iteratively denoising a sparse contour representation. We introduce multiple novel adaptations for improved low-data efficacy and inference efficiency, including using a simplified diffusion process, a customized model architecture, and minimal post processing to produce a dense, isolated contour given a dataset of size <500 training images. Our method outperforms several SOTA baselines on the medical imaging dataset KVASIR, is competitive on HAM10K and our custom wildfire dataset, Smoke, while improving inference framerate by 3.5X.

翻译：不规则与半透明物体的边界检测是一个重要问题，在医学影像、环境监测和制造业等领域具有广泛应用，而这些应用常受限于标注数据稀缺和现场计算资源不足。尽管近期图像分割研究主要关注分割掩码与真实标注的对齐，边界检测任务——尤其是在低数据量条件下——仍未得到充分研究。本文提出一种轻量化的离散扩散轮廓精细化流程，用于在低数据量条件下实现鲁棒的边界检测。我们采用带有自注意力层的卷积神经网络（CNN）架构作为流程核心，并以分割掩码为条件，对稀疏轮廓表示进行迭代去噪。为提高低数据量下的效能与推理效率，我们引入了多项创新性改进，包括简化的扩散过程、定制化的模型架构以及极简的后处理步骤，从而在训练图像数量少于500的数据集上生成稠密且孤立的轮廓。我们的方法在医学影像数据集KVASIR上优于多个先进基线模型，在HAM10K及我们自定义的野火数据集Smoke上表现具有竞争力，同时将推理帧率提升了3.5倍。