Biomedical image classification with random subwindows and decision trees

peer reviewedIn this paper, we address a problem of biomedical image classification that involves the automatic classification of x-ray images in 57 predefined classes with large intra-class variability. To achieve that goal, we apply and slightly adapt a recent generic method for image classification based on ensemble of decision trees and random subwindows. We obtain classification results close to the state of the art on a publicly available database of 10000 x-ray images. We also provide some clues to interpret the classification of each image in terms of subwindow relevance

A web-based framework for visualization, annotation, and automatic exploitation of high-resolution bioimages using tree-based machine learning methods

CYTOMIN

An approach for detection of glomeruli in multisite digital pathology

peer reviewedWe present a novel bioimage informatics workflow that combines Icy and Cytomine software and their algorithms to enable large-scale analysis of digital slides from multiple sites. In particular, we apply this workflow on renal biopsies and evaluate empirically our approach for the automatic detection of glomeruli in hundreds of tissue sections

Détection automatique de glomérules en pathologie digitale

Dans cet article, nous proposons une méthodologie de détection de glomérules au sein d’images de biopsies rénales. Elle combine des techniques de traitement d’images et d’apprentissage supervisé. Nous évaluons l’approche sur des images présentant plusieurs sources de variations et montrons que les comptages automatiques sont très bien corrélés avec les comptages des expert

Automated multimodal volume registration based on supervised 3D anatomical landmark detection

We propose a new method for automatic 3D multimodal registration based on anatomical landmark detection. Landmark detectors are learned independantly in the two imaging modalities using Extremely Randomized Trees and multi-resolution voxel windows. A least-squares fitting algorithm is then used for rigid registration based on the landmark positions as predicted by these detectors in the two imaging modalities. Experiments are carried out with this method on a dataset of pelvis CT and CBCT scans related to 45 patients. On this dataset, our fully automatic approach yields results very competitive with respect to a manually assisted state-of-the-art rigid registration algorithm

Collaborative analysis of multi-gigapixel imaging data using Cytomine

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications

A rich internet application for remote visualization, collaborative annotation, and automated analysis of large-scale biomages

WIST3 Cytomine 101707

Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species

peer reviewedThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC B

Utilisation de l'auto-apprentissage pour réduire le coût d'annotation pour la segmentation d'image en pathology digitale

peer reviewedData scarcity is a common issue when training deep learning models for digital pathology, as large exhaustively-annotated image datasets are difficult to obtain. In this paper, we propose a self-training based approach that can exploit both (few) exhaustively annotated images and (very) sparsely-annotated images to improve the training of deep learning models for image segmentation tasks. The approach is evaluated on three public and one in-house datasets, representing a diverse set of segmentation tasks in digital pathology. The experimental results show that self-training allows to bring significant model improvement by incorporating sparsely annotated images and proves to be a good strategy to relieve labeling effort in the digital pathology domain