标题： 重叠感知的分割用于遮挡物体的拓扑重建 显示英文标题

标题： Overlap-aware segmentation for topological reconstruction of obscured objects

摘要： 重叠物体的分离在科学成像中是一个重大挑战。 虽然深度学习分割-回归算法可以预测像素级强度，但它们通常将所有区域同等对待，而不是优先考虑归属最模糊的重叠区域。 实例分割的最新进展表明，在训练中对像素重叠区域进行加权可以改善重叠区域的分割边界预测，但这一想法尚未扩展到分割回归。 我们通过重叠感知图像分割（OASIS）来解决这个问题：一种新的分割-回归框架，其加权损失函数旨在训练过程中优先考虑物体重叠区域，从而能够从被严重遮挡的物体中提取像素强度和拓扑特征。 我们在MIGDAL实验的背景下展示了OASIS，该实验旨在直接成像Migdal效应——一种由核散射引起的罕见电子发射过程——在一个低压光学时间投影室中。 在这种情况下，重建目标是一个微弱的电子反冲轨迹，它通常被数量级更亮的核反冲轨迹严重掩盖。 与未加权训练相比，OASIS将低能电子轨迹（4-5 keV）的中位数强度重建误差从-32%提高到-14%，并将拓扑交并比分数从0.828提高到0.855。 这些性能提升证明了OASIS在重叠主导区域恢复被遮挡信号的能力。 该框架为科学成像提供了一种可推广的方法，其中像素代表物理量，重叠遮蔽了感兴趣的特征。 所有代码均可公开获取，以促进跨领域的应用。 显示英文摘要

摘要： The separation of overlapping objects presents a significant challenge in scientific imaging. While deep learning segmentation-regression algorithms can predict pixel-wise intensities, they typically treat all regions equally rather than prioritizing overlap regions where attribution is most ambiguous. Recent advances in instance segmentation show that weighting regions of pixel overlap in training can improve segmentation boundary predictions in regions of overlap, but this idea has not yet been extended to segmentation regression. We address this with Overlap-Aware Segmentation of ImageS (OASIS): a new segmentation-regression framework with a weighted loss function designed to prioritize regions of object-overlap during training, enabling extraction of pixel intensities and topological features from heavily obscured objects. We demonstrate OASIS in the context of the MIGDAL experiment, which aims to directly image the Migdal effect--a rare process where electron emission is induced by nuclear scattering--in a low-pressure optical time projection chamber. This setting poses an extreme test case, as the target for reconstruction is a faint electron recoil track which is often heavily-buried within the orders-of-magnitude brighter nuclear recoil track. Compared to unweighted training, OASIS improves median intensity reconstruction errors from -32% to -14% for low-energy electron tracks (4-5 keV) and improves topological intersection-over-union scores from 0.828 to 0.855. These performance gains demonstrate OASIS's ability to recover obscured signals in overlap-dominated regions. The framework provides a generalizable methodology for scientific imaging where pixels represent physical quantities and overlap obscures features of interest. All code is openly available to facilitate cross-domain adoption.