Ground-truth cell body segmentation used for Starfinity training

Accurate segmentation of volumetric fluorescence image data has been a long-standing challenge and it can considerably degrade the accuracy of multiplexed fluorescence in situ hybridization (FISH) analysis. To overcome this challenge, we developed a deep learning-based automatic 3D segmentation algorithm, called Starfinity. It first predicts its cell center probability and its radial distances to the nearest cell borders for each pixel. It then aggregates pixel affinity maps from the densely predicted distances and applies a watershed segmentation on the affinity maps using the thresholded center probability as seeds.This repository contains (1) 'ground-truth' segmentation annotation used to train the Starfinity model, (2) the trained Starfinity model used to predict segmentation masks for EASI-FISH data from the lateral hypothalamus (LHA). DAPI-stained RNA images collected from the Zeiss Z.1 lightsheet microscope after expansion (ExM) were used. The manual segmentation was performed using Paintera on full-resolution (0.23µm x 0.23µm x 0.42µm) images. The raw and annotated images were then down sampled 4x4x2 (0.92µm x 0.92µm x 0.84µm) for training and prediction.Manual inspection of the predicted segmentation from ~5% of cells in 4 LHA samples (a total of ~4,000 out of 80,000 cells) suggests that 93% of cells were properly segmented with this model.

体积荧光图像数据的精准分割长期以来都是一项极具挑战性的难题，且会显著降低多重荧光原位杂交（Multiplexed Fluorescence In Situ Hybridization, FISH）分析的准确性。为解决这一难题，我们开发了一款基于深度学习的全自动三维分割算法，命名为Starfinity。该算法首先对每个像素预测其细胞中心概率，以及该像素到最近细胞边界的径向距离；随后，算法将密集预测得到的距离信息聚合为像素亲和力图，并以阈值化后的细胞中心概率作为种子点，对亲和力图实施分水岭分割操作。
本数据集仓库包含以下内容：「(1) 用于训练Starfinity模型的真值（ground-truth）分割标注集」；「(2) 用于预测下丘脑外侧区（Lateral Hypothalamus, LHA）的EASI-FISH数据分割掩码的已训练Starfinity模型」。本次实验使用了经扩展显微镜（Expansion Microscopy, ExM）处理后、由蔡司Z.1光片显微镜采集的DAPI染色RNA图像。手动分割操作采用Paintera工具在全分辨率（0.23µm × 0.23µm × 0.42µm）图像上完成。随后，原始图像与标注图像均按4×4×2的比例下采样（分辨率变为0.92µm × 0.92µm × 0.84µm）以用于模型训练与预测。
通过对4个LHA样本中约5%的细胞（共计80000个细胞中的约4000个）的预测分割结果进行人工检查，结果显示该模型可对93%的细胞实现准确分割。