Benoît Naegel

Semantic segmentation has suffered for a while from a lack of datasets such as ImageNet for image classification. This issue was partially alleviated by the advent of the segment anything model (SAM), which provides a foundation model trained on the largest and most diverse segmentation dataset to date. However, the SAM often falls short in segmenting specific regions, mostly in regard to biomedical images; this is why unsupervised domain adaptation (UDA) remains the best option for addressing the challenge of generalization capabilities. Classical UDA methods might be ineffective in several biomedical segmentation cases because the gap between two datasets, named domain shift, is too high. To address this issue, we propose a strategy based on learning the source mask probability distribution with a segmentation diffusion model as a generative prior to propose accurate target segmentation at inference. This latter can be guided by supplementary inputs, which allows us to call for the rich information contained in SAM raw segmentation both to perform adaptation and to improve robustness. A study was conducted using a comprehensive collection of segmentation datasets: 3 domains for mitochondria, 2 for the endoplasmic reticulum, and 2 for brain tumors, allowing the creation of 10 adaptation scenarios and providing an extensive test basis. The results of the experiments reveal that our proposed method outperforms various state-of-the-art UDA methods. Furthermore, ablation studies highlight the significant role of each component of our presented strategy. The code will be made available.

Deep learning methods using convolutional neural networks are very effective for automatic image segmentation tasks with no exception for cellular electron micrographs. However, the lack of dedicated easy‐to‐use tools largely reduces the widespread use of these techniques. Here we present DeepSCEM, a straightforward tool for fast and efficient segmentation of cellular electron microscopy images using deep learning with a special focus on efficient and user‐friendly generation and training of models for organelle segmentation.

The segmentation of the bronchus tree and pulmonary arteries and veins in CT scans plays an important role in patient care for both diagnosis and treatment phases. The extraction of theses tubular structures using either manual or interactive segmentation tools is time-consuming and prone to error due to the complexity of aerial and vascular trees. In this work, we propose a fully automatic method, relying on cascaded convolutional neural networks, to tackle lungs, bronchus and pulmonary arteries and veins segmentation. The first component based on a 2DU-Net architecture is dedicated to right and left lung segmentation. The second component relies on a three-paths 2.5D fully convolutional networks along axial, coronal and sagittal slices and focuses on tubular structures. Performance is assessed on a database of 193 chest CT scans.

In biology and pathology, immunofluorescence microscopy approaches are leading techniques for deciphering molecular mechanisms of cell activation and disease progression. Although several solutions for image analysis exist, totally nonsubjective image analysis remains difficult. There is therefore a strong need for analysis procedures highly reproducible, avoiding thresholds and selection of objects of interest by hand. To address this need, we describe a fully automatic segmentation of cell nuclei in 2-D immunofluorescence images. The method merges segments of the image to fit with a nuclei model learned by a trained Random Forest classifier. The merging procedure explores efficiently the fusion configurations space of an over-segmented image by using minimum spanning tree of its region adjacency graph.