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 is available at: https://github.com/alex-stenger/GUDA.

Unsupervised Domain Adaptation (UDA) methods have emerged as a promising solution to generalize a learning to close datasets (domains) without the need to produce new ground truth. Nonetheless in biomedical images, some high domain shifts between source and target images lead to poor adaptation. To address this issue, we propose a method relying on two main ideas. First, we learn the source posterior label distribution with diffusion models. Assuming the target label distribution is similar, this learning helps us to guide the diffusion process to generate relevant segmentation masks on target domain. Alongside this probabilistic constraint, we propose a reconstruction pretext task on both source and target domain to extract common images features. Our approach is compared to the state of the art on three highly shifted mitochondria segmentation datasets. Our method ranks among the best in moderately difficult adaptation cases and succeeds in difficult adaptation cases where all other tested methods fail. Code will be available.

Large annotated datasets and new models have led to significant improvements in supervised semantic segmentation. On the other side, Unsupervised Domain Adaptation (UDA) for Semantic Segmentation is still an arduous open research topic. While new ideas frequently come out based on recent findings, best methods still rely on basic techniques such as the use of pseudo-labels on target for self-training. Nonetheless, such methods fail when applied to difficult UDA cases like Biomedical Images where the domain shift is too high, leading to pseudo-labels of poor quality. In this work, we propose RESAMPL-UDA ( RE fined SAM -based P seudo L abel UDA ), an unsupervised domain adaptation method that effectively integrates zero-shot predictions from the Segment Anything (SAM) model. Given the high complexity and variability of biomedical images, SAM alone often produces detailed segmentations without necessarily capturing the intended structures. To address this, our method involves training a dedicated refinement network on source domain data to selectively enhance SAM-generated masks. These refined segmentations then serve as reliable pseudo-labels within our UDA framework, significantly facilitating the adaptation process. Experiments on 8 adaptation cases demonstrate that our method outperforms the state of the art. In addition, we extend successfully our work to Source-Free Unsupervised Domain Adaptation, demonstrating its versatility. The code will be made available.

The analysis of axillary and supraclavicular lymph nodes is a primary prognostic factor for the staging of breast cancer. However, due to the size of lymph nodes and the low resolution of PET data, their segmentation is challenging. We investigate the relevance of considering axillary and supraclavicular lymph node segmentation from PET/CT images by coupling Convolutional Neural Networks (CNNs) and Component-Trees (C-Trees). Building upon the U-Net architecture, we propose a framework that couples a multi-modal U-Net fed with PET and CT with a hierarchical model obtained from the PET that provides additional high-level region-based features as input channels. Our working hypotheses are twofold. First, we take advantage of both anatomical information from CT for detecting the nodes, and functional information from PET for detecting the pathological ones. Second, we consider region-based attributes extracted from C-Tree analysis of 3D PET/CT images to improve the CNN segmentation. We carried out experiments on a dataset of 240 pathological lymph nodes from 52 patients scans, and compared our outputs with human expert-defined ground-truth, leading to promising results.

Curvilinear structure restoration in image processing procedures is a difficult task, which can be compounded when these structures are thin, i.e., when their smallest dimension is close to the resolution of the sensor. Many recent restoration methods involve considering a local gradient-based regularization term as prior, assuming gradient sparsity. An isotropic gradient operator is typically not suitable for thin curvilinear structures, since gradients are not sparse for these. In this paper, we propose a mixed gradient operator that combines a standard gradient in the isotropic image regions, and a directional gradient in the regions where specific orientations are likely. In particular, such information can be provided by curvilinear structure detectors (e.g., RORPO or Frangi filters). Our proposed mixed gradient operator, that can be viewed as a companion tool of such detectors, is proposed in a discrete framework and its formulation/computation holds in any dimension; in other words, it is valid in [Formula: see text], n ≥ 1 . We show how this mixed gradient can be used to construct image priors that take edge orientation, as well as intensity, into account, and then involved in various image processing tasks while preserving curvilinear structures. The experiments carried out on 2D, 3D, real, and synthetic images illustrate the relevance of the proposed gradient, and its use in variational frameworks for both denoising and segmentation tasks.

Inter / intra operator errors and high-time consumption induced by manual delineation, are the main drawbacks nowadays in clinical PET tumor segmentation. Several methodologies have been proposed to automate this task. However, there is not yet a validated general protocol to use in clinical routine. Multimodality imaging has been shown to provide good performance, taking into account both functional and anatomical scopes together for segmentation decision. In this context, the involved images used are generally required to be spatially corresponding. However, this is not always the case due to acquisition constraints or for multidate follow-up. In this work, we propose a spatially independent algorithm that avoids image pre-processing (e.g. image registration) or acquisition adjustments for multimodal segmentation. In particular, non-spatially correspondent images (such as multitemporal ones) can be directly exploited taking advantage of hierarchical image structure properties. Regions, obtained from hierarchical models of images, are co-evaluated to match similar ones such as tumors on PET and CT. Results show good performance in terms of time-computing and robust-nesses dealing with PET/CT segmentation problems such as necrosis, compared with other methodologies.

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.

Angiographic imaging is a crucial domain of medical imaging. In particular, Magnetic Resonance Angiography (MRA) is used for both clinical and research purposes. This article presents the first framework geared toward the design of virtual MRA images from real MRA images. It relies on a pipeline that involves image processing, vascular modeling, computational fluid dynamics and MR image simulation, with several purposes. It aims to provide to the whole scientific community (1) software tools for MRA analysis and blood flow simulation; and (2) data (computational meshes, virtual MRAs with associated ground truth), in an open-source/open-data paradigm. Beyond these purposes, it constitutes a versatile tool for progressing in the understanding of vascular networks, especially in the brain, and the associated imaging technologies. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The increasing availability of digital whole slide images opens new perspectives for computer-assisted image analysis complementing modern histopathology, assuming we can implement reliable and efficient image analysis algorithms to extract the biologically relevant information. Both validation and supervised learning techniques typically rely on ground truths manually made by human experts. However, this task is difficult, subjective and usually not exhaustive. This is a well-known issue in the field of biomedical imaging, and a common solution is the use of artificial “phantoms”. Following this trend, we study the feasibility of synthesizing artificial histological images to create perfect ground truths. In this paper, we show that it is possible to generate a synthetic whole slide image with reasonable computing resources, and we propose a way to evaluate its quality.

We introduce the new notion of multivalued component-tree, that extends the classical component-tree initially devoted to grey-level images, in the mathematical morphology framework. We prove that multivalued component-trees can model images whose values are hierarchically organized. We also show that they can be efficiently built from standard component-tree construction algorithms, and involved in antiextensive filtering procedures. The relevance and usefulness of multivalued component-trees is illustrated by an applicative example on hierarchically classified remote sensing images.

Mathematical morphology, initially devoted to binary and grey-level image processing, also offers opportunities to develop efficient tools for multivalued - and in particular, colour - images. In this context, connected operators are increasingly considered as a relevant way to obtain such tools, mainly for image filtering and segmentation purposes. In this article, we focus on connected operators based on component-trees and their extension to multivalued images, namely component-graphs. Beyond the classical colour-handling strategies, we show how component-graphs can be algorithmically used to efficiently handle the whole structural information gathered by colour spaces, in order to finally design original image filtering tools.

In recent papers, a new notion of component-graph was introduced. It extends the classical notion of component-tree initially proposed in mathematical morphology to model the structure of gray-level images. Component-graphs can indeed model the structure of any-gray-level or multivalued-images. We now extend the antiextensive filtering scheme based on component-trees, to make it tractable in the framework of component-graphs. More precisely, we provide solutions for building a component-graph, reducing it based on selection criteria, and reconstructing a filtered image from a reduced component-graph. In this paper, we first consider the cases where component-graphs still have a tree structure; they are then called multivalued component-trees. The relevance and usefulness of such multivalued component-trees are illustrated by applicative examples on hierarchically classified remote sensing images.

This article focuses on the tomographic reconstruction of 2D binary images from projections whose directions are unknown. Observing that there is a mutual influence between the image reconstruction and the directions reconstruction, a new ab-initio method based on joint reconstruction of directions and image is proposed for 2D binary objects. Its implementation is done by optimizing a cost function taking as arguments the input projections, the current reconstructed image and the current directions. The optimization of the cost function is based on simulated annealing. After detailing the cost function, we test the proposed method for different resolutions and different noise levels. Finally, we present the future development of this promising method.

An original binarization method based on connected operators is proposed in this paper. Connected operators enable to filter and/or segment an image by preserving its contours.The proposed binarization method enables to extract relevant document objects by means of the component-tree structure. This method was compared to other binarization methods and showed good behavior in various contexts.

Component-trees provide efficient ways to define filtering-based procedures on grey-level images. We propose an extension of the notion of component-trees to the case of ¿non grey-level¿ images (i.e. images taking their values in partially-ordered sets) including in particular - but not exclusively - colour images. Experiments performed on such images emphasise the interest of the approach.