Dataset Information

A Manual Segmentation Tool for Three-Dimensional Neuron Datasets.

ABSTRACT: To date, automated or semi-automated software and algorithms for segmentation of neurons from three-dimensional imaging datasets have had limited success. The gold standard for neural segmentation is considered to be the manual isolation performed by an expert. To facilitate the manual isolation of complex objects from image stacks, such as neurons in their native arrangement within the brain, a new Manual Segmentation Tool (ManSegTool) has been developed. ManSegTool allows user to load an image stack, scroll down the images and to manually draw the structures of interest stack-by-stack. Users can eliminate unwanted regions or split structures (i.e., branches from different neurons that are too close each other, but, to the experienced eye, clearly belong to a unique cell), to view the object in 3D and save the results obtained. The tool can be used for testing the performance of a single-neuron segmentation algorithm or to extract complex objects, where the available automated methods still fail. Here we describe the software's main features and then show an example of how ManSegTool can be used to segment neuron images acquired using a confocal microscope. In particular, expert neuroscientists were asked to segment different neurons from which morphometric variables were subsequently extracted as a benchmark for precision. In addition, a literature-defined index for evaluating the goodness of segmentation was used as a benchmark for accuracy. Neocortical layer axons from a DIADEM challenge dataset were also segmented with ManSegTool and compared with the manual "gold-standard" generated for the competition.

SUBMITTER: Magliaro C

PROVIDER: S-EPMC5450622 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Manual Segmentation Tool for Three-Dimensional Neuron Datasets.

Magliaro Chiara C Callara Alejandro L AL Vanello Nicola N Ahluwalia Arti A

Frontiers in neuroinformatics 20170531

To date, automated or semi-automated software and algorithms for segmentation of neurons from three-dimensional imaging datasets have had limited success. The gold standard for neural segmentation is considered to be the manual isolation performed by an expert. To facilitate the manual isolation of complex objects from image stacks, such as neurons in their native arrangement within the brain, a new Manual Segmentation Tool (ManSegTool) has been developed. ManSegTool allows user to load an image ...[more]

PMID: 28620293

Similar Datasets

Project description:Introduction: In several fields, the process of fusing multiple two-dimensional (2D) closed lines is an important step. For instance, this is fundamental in histology and oncology in general. The treatment of a tumor consists of numerous steps and activities. Among them, segmenting the cancer area, that is, the correct identification of its spatial location by the segmentation technique, is one of the most important and at the same time complex and delicate steps. The difficulty in deriving reliable segmentations stems from the lack of a standard for identifying the edges and surrounding tissues of the tumor area. For this reason, the entire process is affected by considerable subjectivity. Given a tumor image, different practitioners can associate different segmentations with it, and the diagnoses produced may differ. Moreover, experimental data show that the analysis of the same area by the same physician at two separate timepoints may result in different lines being produced. Accordingly, it is challenging to establish which contour line is the ground truth. Methods: Starting from multiple segmentations related to the same tumor, statistical metrics and computational procedures could be exploited to combine them for determining the most reliable contour line. In particular, numerous algorithms have been developed over time for this procedure, but none of them is validated yet. Accordingly, in this field, there is no ground truth, and research is still active. Results: In this work, we developed the Two-Dimensional Segmentation Fusion Tool (TDSFT), a user-friendly tool distributed as a free-to-use standalone application for MAC, Linux, and Windows, which offers a simple and extensible interface where numerous algorithms are proposed to "compute the mean" (i.e., the process to fuse, combine, and "average") multiple 2D lines. Conclusions: The TDSFT can support medical specialists, but it can also be used in other fields where it is required to combine 2D close lines. In addition, the TDSFT is designed to be easily extended with new algorithms thanks to a dedicated graphical interface for configuring new parameters. The TDSFT can be downloaded from the following link: https://sourceforge.net/p/tdsft.

Dataset Information

A Manual Segmentation Tool for Three-Dimensional Neuron Datasets.

Publications

A Manual Segmentation Tool for Three-Dimensional Neuron Datasets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets