Datasets used in "3-D quantification of filopodia in motile cancer cells" IEEE Transactions on Medical Imaging 38(3), 862-872 (2019)

These datasets were used to produce the results of the following TMI paper: "3D Quantification of Filopodia in Motile Cancer Cells", Castilla C., et al. (2019). IEEE Transactions on Medical Imaging 38(3):862,872. 3D+t image real and synthetic sequences of cells of the A549 lung adenocarcinoma cancer cell line, displaying three different phenotypes of CRMP-2, a protein involved in the assembly and disassembly of actin filaments. All real videos were acquired on aUltraviewERS (Perkin Elmer, Inc., Waltham, MA, USA), spinning disk confocal microscope, using the 488 nm line of an Ar/Kr laser to image the sample through a Plan-Apochromatic 63x 1.20 NA water immersion objective lens (Carl Zeiss, AG., Wetzlar, Germany). The videos contained one cell, imaged every two minutes duringone hour. The original image voxel size of 0.126x0.126x1.0 µm was resampled in the axial direction using cubic spline interpolation to obtain isotropic image data with a voxel size of 0.12x0.126x0.126 µm. The synthetic videos were produced using our recent cell simulator [1], to reproduce as closely as possible the image properties and cell phenotypes of the real videos. Along with the datasets, we provide AVI videos displaying the results of maximum intensity projections (MIP) of the segmentation of the cells using two segmenation methods, one based on the minimization of the Chan-Vese model (CVS) and a 3D Convolutional Neural Network (CNN) The file names encode the following information: CRMP2 phenotype and video type:- WTR: Wild type, real video- OER: Over expressing, real video- PDR: Phospho-defective, real video- WTS: Wild type, synthetic video- OES: Over expressing, synthetic video- PDS: Phospho-defective, synthetic video Segmentation method:- CVS: Minimization of the Chan-Vese models- CNN: Convolutional Neural Network Video number:- 01- 02- 03 [1] D. V. Sorokin, I. Peterlík, V. Ulman, D. Svoboda and M. Maška, “Model-based generation of synthetic 3D time-lapse sequences of motile cells with growing filopodia,” In IEEE International Symposium on Biomedical Imaging, pp. 822–826, 2017

本数据集用于生成以下TMI论文的研究成果：《移动癌细胞中细丝的3D量化》，作者为Castilla C.等人（2019年）。该论文发表于《IEEE医学成像杂志》第38卷第3期，页码为862至872。数据集包含A549肺腺癌细胞系的三种不同CRMP-2表型（一种参与肌动蛋白丝组装与解组装的蛋白）的3D+t真实和合成细胞图像序列。所有真实视频均由UltraviewERS（Perkin Elmer，Inc.，Waltham，MA，USA）旋转盘共聚焦显微镜获取，使用Ar/Kr激光的488 nm波线通过Plan-Apochromatic 63x 1.20 NA水浸物镜（Carl Zeiss，AG.，Wetzlar，Germany）成像样本。视频包含单个细胞，每小时每两分钟进行一次成像。原始图像体素大小为0.126x0.126x1.0 µm，通过轴向上的立方样条插值重采样，以获得具有0.12x0.126x0.126 µm体素尺寸的各向同性图像数据。合成视频采用我们最近开发的细胞模拟器[1]制作，旨在尽可能精确地重现真实视频的图像特性和细胞表型。此外，我们提供了显示两种分割方法（一种基于Chan-Vese模型最小化（CVS）和3D卷积神经网络（CNN））的细胞分割最大强度投影（MIP）结果的AVI视频。文件名编码了以下信息：CRMP2表型和视频类型：- WTR：野生型，真实视频- OER：过表达，真实视频- PDR：磷酸化缺陷型，真实视频- WTS：野生型，合成视频- OES：过表达，合成视频- PDS：磷酸化缺陷型，合成视频；分割方法：- CVS：Chan-Vese模型最小化- CNN：卷积神经网络；视频编号：- 01- 02- 03 [1] D. V. Sorokin，I. Peterlík，V. Ulman，D. Svoboda和M. Maška，“基于模型的合成移动细胞生长细丝的3D时间序列生成，”IEEE国际生物医学成像研讨会，第822–826页，2017。