PartSeg: a tool for quantitative feature extraction from 3D microscopy images for dummies

Abstract

Background: Bioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation.

Results: In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures.

Conclusions: In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.

Keywords: 3D FISH; 3D reconstruction; Batch processing; Bioimaging; Chromatin; Electron microscopy; Nucleus; Segmentation; Super-resolution microscopy.

Fig. 1

Use cases showing examples of PartSeg analysis performed on 3D confocal images of rat hippocampal neurons and mouse neuroblastoma cell line. A. Distribution of chromosome 1 and 16 territories (CTs) in a neuronal nucleus. A.a Confocal picture of a single, segmented nucleus with CT 1 (upper panel, green) and CT 16 (lower panel, red) visualised by FISH. A.b Quantification of volume and surface of CT 1 (segmented with MSO algorithm) and CT 16 (segmented with manual threshold). A.c Radial distribution of CTs 1 and 16 volume within a neuronal nucleus. Concentric spheres depicted for chromosome 1 as A, B and C have an equal radius. A.d Perinuclear distribution of CT 1 and 16 . CT volume located within 1500 nm from the nucleus boundary (depicted for chromosome 16) was measured for each chromosome. B Distribution of Npas4 gene within a neuronal nucleus and chromosome 1 CT. B.a Confocal picture of a single, segmented nucleus with chromosome 1 (depicted in red) and Npas4 gene (depicted in green) visualised by FISH. B.b Segmentation of Npas4 gene and its localization in a neuronal nucleus. B.c Segmentation of Npas 4 gene and chromosome 1 CT, graphs show Npas4 localisation relative to CT. C Neuronal nucleus and chromatin analysis. C.a DNA staining of a neuronal nucleus. C.b Segmentation of neuronal nucleus based on DNA staining. Quantification of nuclear diameter and volume. C.c Quantification of average DNA volume based on segmentation using Otsu automated threshold. C.d Quantification of chromocenters based on segmentation using Rényi Entropy automated threshold, graphs show chromocenters number, relative volume, and percentage of chromocenters volume localised within 800 nm from the nuclear border. D Quantification of PML bodies. D.a Confocal picture of a single, segmented nucleus of a mouse neuroblastoma cell line with PML bodies depicted in red. D.b Segmentation of PML bodies and quantification of their number, diameter, and volume

Fig. 2

GUI overview a ROI Mask—GUI for nucleus segmentation and preselection, b ROI analysis—main GUI for analysis of single cases and preparation of parameters for batch processing, c measurement selection. d Data source, e batch processing f measurements calculated during batch processing saved in excel

Fig. 3

Watershed and multiscale opening comparison. Difference between object separation with Multiscale Opening and Watershed transform. a Confocal picture of a single, segmented nucleus with the FISH signal from a chromosome 1 probe (depicted in green). b Segmentation of chromosome 1 signal using the Watershed algorithm. c Segmentation of chromosome 1 signal using the Multiscale Opening algorithm

Fig. 4

Sample protocol from Icy. It contains approximation, using blocks from a standard installation of Thresholding with watershed ROI extraction method from "ROI extraction"