Edinburgh EIT Dataset

This dataset is for publication "Structure-Aware Dual-Branch Network for Electrical Impedance Tomography in Cell Culture Imaging", and "Hybrid Learning-Based Cell Aggregate Imaging With Miniature Electrical Impedance Tomography". ABSTRACT: Electrical impedance tomography (EIT) is an emerging imaging modality to monitor 3D cell culture dynamics through reconstructing the electrical properties of cell clusters. Recently, machine-learning (ML)-based approaches have achieved significant gains for the image reconstruction of EIT against conventional physical model-based methods. However, continuous, multilevel conductivity distributions, which commonly exists in cell culture imaging, are more rigorous to reconstruct and remains challenging. This study aims to tackle this challenge by proposing a structure-aware dual-branch deep-learning method to predict both structure distribution and conductivity values. The proposed network comprises two independent branches to encode the structure and conductivity features, respectively. The two branches are jointed later to make final predictions of conductivity distributions. Numerical and experimental evaluation results demonstrate the superior performance of the proposed method in dealing with the multilevel, continuous conductivity reconstruction problem.本数据集配套两篇发表论文，分别为《面向细胞培养成像的电阻抗断层成像结构感知双分支网络》与《基于混合学习的微型电阻抗断层成像细胞聚集体成像》。摘要：电阻抗断层成像（Electrical Impedance Tomography，EIT）是一种新兴成像模态，可通过重建细胞团的电学特性实现对三维细胞培养动态过程的监测。近年来，基于机器学习（Machine Learning，ML）的方法相较于传统基于物理模型的方法，在电阻抗断层成像的图像重建任务中已取得显著进展。然而，细胞培养成像中普遍存在的连续多层级电导率分布，其重建难度更高，仍是一项极具挑战性的问题。本研究针对该难题提出一种结构感知双分支深度学习方法，可同时预测结构分布与电导率数值。所提网络包含两个独立分支，分别用于编码结构特征与电导率特征，后续将两个分支融合以完成电导率分布的最终预测。数值与实验评估结果表明，所提方法在处理多层级连续电导率重建问题时性能优异。

搜集汇总

背景与挑战

背景概述

该数据集是爱丁堡大学发布的用于电阻抗断层扫描（EIT）在细胞培养成像中的研究数据集，支持两篇相关出版物，聚焦于通过结构感知双分支深度学习网络解决多级连续电导率分布的重建挑战。数据集包含实验和数值评估数据，旨在促进EIT图像重建方法的开发与优化。