迈向高质量图像分割：通过惩罚邻居像素提升拓扑准确性
Towards High-Quality Image Segmentation: Improving Topology Accuracy by Penalizing Neighbor Pixels
SCNP 是一种即插即用的损失函数增强策略，通过惩罚像素与其最差分类邻居的 logits 来提升分割的拓扑正确性，可方便地集成到现有分割训练流程中，适用于需要保持连通分量数量的医学或遥感等分割任务。
原文依据 We present SCNP, an efficient method that improves topology accuracy by penalizing the logits with their poorest-classified neighbor
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论用于图像分割的标准深度学习模型无法保证拓扑准确性，不能保持正确的连通分量或结构数量。这进而影响分割质量，并损害后续定量分析的可靠性。先前的工作提出了专门的框架、架构和损失函数来增强拓扑准确性。然而，这些方法通常难以集成到现有的训练流程中，计算成本非常高，或者局限于管状形态的结构。我们提出了 SCNP，一种高效的方法，通过用像素分类最差的邻居来惩罚其 logits，迫使模型在允许改进像素本身之前先改进像素邻居的预测，从而提升拓扑准确性。我们在涵盖不同结构形态和图像模态的 13 个数据集上展示了 SCNP 的有效性，并将其集成到三个用于语义分割和实例分割的框架中。此外，我们表明 SCNP 可以集成到多种损失函数中，使它们提升拓扑准确性。我们的代码可在 https://jmlipman.github.io/SCNP-SameClassNeighborPenalization 获取。
英文原文摘要 1272 字符
Standard deep learning models for image segmentation cannot guarantee topology accuracy, failing to preserve the correct number of connected components or structures. This, in turn, affects the quality of the segmentations and compromises the reliability of the subsequent quantification analyses. Previous works have proposed to enhance topology accuracy with specialized frameworks, architectures, and loss functions. However, these methods are often cumbersome to integrate into existing training pipelines, they are computationally very expensive, or they are restricted to structures with tubular morphology. We present SCNP, an efficient method that improves topology accuracy by penalizing the logits with their poorest-classified neighbor, forcing the model to improve the prediction at the pixels' neighbors before allowing it to improve the pixels themselves. We show the effectiveness of SCNP across 13 datasets, covering different structure morphologies and image modalities, and integrate it into three frameworks for semantic and instance segmentation. Additionally, we show that SCNP can be integrated into several loss functions, making them improve topology accuracy. Our code can be found at https://jmlipman.github.io/SCNP-SameClassNeighborPenalization.