E. J. Delp

Synthetic Echocardiograms Generation Using Diffusion Models

This paper introduces a video diffusion model for generating synthetic echocardiograms with higher resolution and fewer artifacts than existing methods.

Ensemble Processing and Synthetic Image Generation For Abnormally Shaped Nuclei Segmentation

This paper proposes a system for segmenting abnormally shaped nuclei using synthetic data and ensemble strategies, achieving better performance than existing methods.

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

The paper provides 3D microscopy volumes with partial manual annotations and synthetically generated volumes for training segmentation methods.

3D CentroidNet: Nuclei Centroid Detection With Vector Flow Voting

This paper introduces a 3D nuclei centroid detection method using vector flow voting, achieving better performance than existing methods on real microscopy data.

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

NISNet3D is a deep learning model for 3D nuclear segmentation in fluorescence microscopy images, trained on synthetic data and compared to eight existing methods.

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

DINAVID provides a cloud-based system for remote 3D microscopy image analysis and visualization, designed to handle large datasets and improve accessibility.

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

This paper introduces EMR-CNN, a 3D nuclei segmentation method that avoids ground truth annotations and uses ensemble learning and slice fusion for efficient and accurate results.

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

The paper uses image-based phenotyping to identify new genetic loci for disease resistance in tomato plants, detecting QTL earlier and more accurately than human assessment.