Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets.

Recent advances in computer vision have led to significant progress in the generation of realistic image data, with denoising diffusion probabilistic models proving to be a particularly effective method. In this study, we demonstrate that diffusion models can effectively generate fully-annotated microscopy image data sets through an unsupervised and intuitive approach, using rough sketches of desired structures as the starting point. The proposed pipeline helps to reduce the reliance on manual annotations when training deep learning-based segmentation approaches and enables the segmentation of diverse datasets without the need for human annotations.

Definition and Quantification of Three-Dimensional Imaging Targets to Phenotype Pre-Eclampsia Subtypes: An Exploratory Study.

Pre-eclampsia is a severe placenta-related complication of pregnancy with limited early diagnostic and therapeutic options. Aetiological knowledge is controversial, and there is no universal consensus on what constitutes the early and late phenotypes of pre-eclampsia. Phenotyping of native placental three-dimensional (3D) morphology offers a novel approach to improve our understanding of the structural placental abnormalities in pre-eclampsia.

Using positional information to provide context for biological image analysis with MorphoGraphX 2.0.

Positional information is a central concept in developmental biology. In developing organs, positional information can be idealized as a local coordinate system that arises from morphogen gradients controlled by organizers at key locations. This offers a plausible mechanism for the integration of the molecular networks operating in individual cells into the spatially coordinated multicellular responses necessary for the organization of emergent forms.

3D fluorescence microscopy data synthesis for segmentation and benchmarking.

Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy image data in a fast and reproducible way. Especially state-of-the-art deep learning-based approaches most often require large amounts of annotated training data to produce accurate and generalist outputs, but they are often compromised by the general lack of those annotated data sets. In this work, we propose how conditional generative adversarial networks can be utilized to generate realistic image data for 3D fluorescence microscopy from annotation masks of 3D cellular structures.

A Multi-Organ Nucleus Segmentation Challenge.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated.

Segmenting renal whole slide images virtually without training data.

Digital pathology is a field of increasing interest and requires automated systems for processing huge amounts of digital data. The development of supervised-learning based automated systems is aggravated by the fact that image properties can change from slide to slide. In this work, the focus is on the segmentation of the glomeruli constituting the most important regions-of-interest in renal histopathology.