Biology subjects

Delp, E. J.

Publications and source records attributed to Delp, E. J..

6 recordsLinked to original sources

bioRxiv2022

3D Ground Truth Annotations of Nuclei in 3D Microscopy Volumes

In this paper we describe a set of 3D microscopy volumes we have partially manually annotated. We describe the volumes annotated and the tools and processes we use to annotate the volumes. In addition, we provide examples of annotated subvolumes. We also provide synthetically generated 3D microscopy volumes that can be used for training segmentation methods. The full set of annotations, synthetically generated volumes, and original volumes can be accessed as described in the paper.

cell biology↗

bioRxiv2022

3D CentroidNet: Nuclei Centroid Detection With Vector Flow Voting

Automated microscope systems are increasingly used to collect large-scale 3D image volumes of biological tissues. Since cell boundaries are seldom delineated in these images, detection of nuclei is a critical step for identifying and analyzing individual cells. Due to the large intra-class variability in nuclei morphology and the difficulty of generating ground truth annotations, accurate nuclei detection remains a challenging task. We propose a 3D nuclei centroid detection method by estimating the "vector flow" volume where each voxel represents a 3D vector pointing to its nearest nuclei centroid in the corresponding microscopy volume. We then use a voting mechanism to estimate the 3D nuclei centroids from the "vector flow" volume. Our system is trained on synthetic microscopy volumes and tested on real microscopy volumes. The evaluation results indicate our method outperforms other methods both visually and quantitatively.

cell biology↗

bioRxiv2022

NISNet3D: Three-Dimensional Nuclear Synthesis and Instance Segmentation for Fluorescence Microscopy Images

The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, researchers generally segment cells by their nuclei. While effective tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task for which few tools have been developed. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present researchers with the opportunity to characterize entire organs. Methods based upon deep-learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper we describe 3D Nuclei Instance Segmentation Network (NISNet3D), a deep learning-based approach in which training is accomplished using synthetic data, profoundly reducing the effort required for network training. We compare results obtained from NISNet3D with results obtained from eight existing techniques.

cell biology↗

bioRxiv2022

DINAVID: A Distributed and Networked Image Analysis System for Volumetric Image Data

BackgroundThe advancement of high content optical microscopy has enabled the acquisition of very large 3D image datasets. Image analysis tools and three dimensional visualization are critical for analyzing and interpreting 3D image volumes. The analysis of these volumes require more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based/cloud-based 3D image processing system. ResultsThe Distributed and Networked Analysis of Volumetric Image Data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists. DINAVID is a server/cloud-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID is designed using open source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis as well as a 3D visualization system. ConclusionsIn this paper, we will present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis.

cell biology↗

bioRxiv2022

An Ensemble Learning and Slice Fusion Strategy for Three-Dimensional Nuclei Instance Segmentation

Automated microscopy image analysis is a fundamental step for digital pathology and computer aided diagnosis. Most existing deep learning methods typically require post-processing to achieve instance segmentation and are computationally expensive when directly used with 3D microscopy volumes. Supervised learning methods generally need large amounts of ground truth annotations for training whereas manually annotating ground truth masks is laborious especially for a 3D volume. To address these issues, we propose an ensemble learning and slice fusion strategy for 3D nuclei instance segmentation that we call Ensemble Mask R-CNN (EMR-CNN) which uses different object detectors to generate nuclei segmentation masks for each 2D slice of a volume and propose a 2D ensemble fusion and a 2D to 3D slice fusion to merge these 2D segmentation masks into a 3D segmentation mask. Our method does not need any ground truth annotations for training and can inference on any large size volumes. Our proposed method was tested on a variety of microscopy volumes collected from multiple regions of organ tissues. The execution time and robustness analyses show that our method is practical and effective.

cell biology↗

bioRxiv2021

Image-based assessment of plant disease progression identifies new genetic loci for resistance

A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best means of disease control is plant resistance, but the identification of genes that promote resistance has been limited by the subjective quantification of disease, which is typically scored by the human eye. We hypothesized that image-based, non-destructive quantification of disease phenotypes would enable the rapid identification of new disease resistance loci. We tested this using the interaction between tomato and Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40,000 time-series images of disease progression in a tomato recombinant inbred line population, and developed an image analysis pipeline providing a suite of ten traits to quantify wilt disease based on plant shape and size. Quantitative trait loci (QTL) analyses using image-based phenotyping identified QTL that were both unique and shared compared with those identified by human assessment of wilting. When shared loci were identified, image-based phenotyping could detect some QTL several days earlier than human assessment. Thus, expanding the phenotypic space of disease with image-based, non- destructive phenotyping allowed both earlier detection and identified new genetic components of resistance.

plant biology↗