Fast and accurate 3-D spine MRI segmentation using FastCleverSeg.

Accurate and efficient segmenting of vertebral bodies, muscles, and discs is crucial for analyzing various spinal diseases. However, traditional methods are either laborious and time-consuming (manual segmentation) or require extensive training data (fully automatic segmentation). FastCleverSeg, our proposed semi-automatic segmentation approach, addresses those limitations by significantly reducing user interaction while maintaining high accuracy.

Time series causal relationships discovery through feature importance and ensemble models.

Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.

Exploring the potential links between gut microbiota composition and natural populations management in wild boar (Sus scrofa).

We surveyed wild boar (Sus scrofa) populations using 16S rRNA gene analysis of the gut microbiota in fresh faeces taken from 88 animals hunted in 16 hunting estates. The wild boar is a very convenient model system to explore how environmental factors including game management, food availability, disease prevalence, and behaviour may affect different biological components of wild individuals with potential implications in management and conservation. We tested the hypotheses that diet (according to stable carbon isotopes analyses), gender (i.

Segmenting Cellular Retinal Images by Optimizing Super-pixels, Multi-level Modularity, and Cell Boundary Representation.

We introduce an interactive method for retina layer segmentation in gray-level and RGB images based on super-pixels, multi-level optimization of modularity, and boundary erosion. Our method produces highly accurate segmentation results and can segment very large images. We have evaluated our method with two datasets of 2D confocal microscopy (CM) images of a mammalian retina.