Daniel Racoceanu
@sorbonne-universite.fr
Paris Brain Institute, Aramis lab
Sorbonne University
Professor in Biomedical Image and Data Computing at Sorbonne University, Paris, and PI at ARAMIS INRIA team / Paris Brain Institute (ICM / Piti-Salpêtrière Hospital), my areas of interest are Medical Image Analysis and Pattern Recognition, my research focusing mainly on Computational Pathology and its Integrative aspects.
EDUCATION
2006 - HDR (Habilitation à Diriger des Recherches), Control and Computer Sciences - University of Franche-Comté, Besançon, France
1997 - Ph.D. - Control and Computer Sciences - University of Franche-Comté, Besançon, France
1993 - M.Sc. (Master of Science) - Control Sciences - University of Technology of Belfort-Montbéliard, France
1992 - Dipl. Ing. (M.Eng. - Master of Engineering) - Mechatronics & Mechanics - Politehnica University of Timisoara, Romania
RESEARCH INTERESTS
Computational Pathology
Biomedical image analysis
Pattern Recognition
Machine Learning
Deep Learning
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Mehdi Ounissi, Morwena Latouche, and Daniel Racoceanu
Springer Science and Business Media LLC
AbstractQuantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases’ characterization. https://github.com/ounissimehdi/PhagoStat.
Xiang Liu, Wanming Hu, Songhui Diao, Deboch Eyob Abera, Daniel Racoceanu, and Wenjian Qin
Elsevier BV
Janan Arslan, Daniel Racoceanu, and Kurt K. Benke
American Medical Association (AMA)
Gabriel Jiménez and Daniel Racoceanu
Springer US
AbstractNoninvasive brain imaging techniques allow understanding the behavior and macro changes in the brain to determine the progress of a disease. However, computational pathology provides a deeper understanding of brain disorders at cellular level, able to consolidate a diagnosis and make the bridge between the medical image and the omics analysis. In traditional histopathology, histology slides are visually inspected, under the microscope, by trained pathologists. This process is time-consuming and labor-intensive; therefore, the emergence of computational pathology has triggered great hope to ease this tedious task and make it more robust. This chapter focuses on understanding the state-of-the-art machine learning techniques used to analyze whole slide images within the context of brain disorders. We present a selective set of remarkable machine learning algorithms providing discriminative approaches and quality results on brain disorders. These methodologies are applied to different tasks, such as monitoring mechanisms contributing to disease progression and patient survival rates, analyzing morphological phenotypes for classification and quantitative assessment of disease, improving clinical care, diagnosing tumor specimens, and intraoperative interpretation. Thanks to the recent progress in machine learning algorithms for high-content image processing, computational pathology marks the rise of a new generation of medical discoveries and clinical protocols, including in brain disorders.
Janan Arslan, Mehdi Ounissi, Haocheng Luo, Matthieu Lacroix, Pierrick Dupré, Pawan Kumar, Arran Hodgkinson, Sarah Dandou, Romain Larive, Christine Pignodel,et al.
SPIE
Cutaneous melanoma is an invasive cancer with a worldwide annual death toll of 57,000 (Arnold et al., JAMA Dermatol 2022). In a metastatic state, surgical interventions are not curative and must be coupled with targeted therapy, or immunotherapy. However, resistance appears almost systematically and late-stage prognosis can remain poor. The complexity to eradicate melanoma stems from its plasticity; these cancer cells continually adapt to the tumor microenvironment, which leads to treatment resistance. Our primary assumption is that therapeutic resistance relies in part on a series of non-genetic transitions including changes in the metabolic states of these cancer cells. The 3D spatial distribution of blood vessels that are sources of nutrition and oxygen that drive this metabolic status is an important variable for understanding zoning aspects of this adaptation process. Using Whole Slide Images (WSI) of melanoma tumors from Patient-Derived Xenograft (PDX) mouse models, we build 3D vascular models to help predict and understand the metabolic states of cancer cells within the tumor. Our 3D reconstruction pipeline was based on PDX tumor samples sectioned over 2mm depth and stained with Hematoxylin and Eosin (H&E). The pipeline involves three primary steps, including 2D vessel segmentation using Deep Learning, intensity- and affine-based image registration, and 3D reconstruction using interpolation and 3D rendering (allowing for better interaction with biologists, pathologists, and clinicians). The originality of our computer-assisted pipeline is its capability to (a) deal with sparse data (i.e., not all tissue sections were readily available), and (b) adapt to a multitude of WSI-related challenges (e.g., epistemic uncertainty, extended processing times due to WSI scale, etc.). We posit both our 3D reconstruction pipeline, quantitative results of the major stages of the process, and a detailed illustration of the challenges faced, presenting resolutions to improve the pipeline’s efficiency.
Gabriel Jimenez Garray, Pablo Mas, Anuradha Kar, Julien Peyrache, Léa Ingrassia, Susana Boluda, Benoit Delatour, Lev Stimmer, and Daniel Racoceanu
SPIE
Recently, high-performance deep learning models have enabled automatic and precise analysis of medical images with high content. In digital histopathology, a challenge lies in analyzing Whole Slide Images (WSI) due to their large size, often requiring splitting them into smaller patches for deep learning models. This leads to the loss of global tissue information and limits the classification or clustering of patients based on tissue characteristics. In this study, we develop a meta-graph approach for semantic spatial analysis of WSI of human brain tissue containing tau protein aggregates, a hallmark of Alzheimer’s disease (AD) in gray matter. Our pipeline extracts morphological features of tau aggregates, such as forming neuritic plaques, and builds a graph based on Delaunay triangulation at the WSI level to extract topological features. This generates morphological and topological data from WSI for patient classification and clustering. We tested this pipeline on a dataset of 15 WSIs from different AD patients. We aim to identify new insights into AD evolution and provide a generic framework for WSI characterization and analysis.
Songhui Diao, Yinli Tian, Wanming Hu, Jiaxin Hou, Ricardo Lambo, Zhicheng Zhang, Yaoqin Xie, Xiu Nie, Fa Zhang, Daniel Racoceanu,et al.
Elsevier BV
Gabriel Jimenez, Anuradha Kar, Mehdi Ounissi, Léa Ingrassia, Susana Boluda, Benoît Delatour, Lev Stimmer, and Daniel Racoceanu
Springer Nature Switzerland
Kristyna Manouskova, Valentin Abadie, Mehdi Ounissi, Gabriel Jimenez, Lev Stimmer, Benoit Delatour, Stanley Durrleman, and Daniel Racoceanu
SPIE
Tau proteins in the gray matter are widely known to be a part of Alzheimer’s disease symptoms. They can aggregate in three different structures within the brain: neurites, tangles, and neuritic plaques. The morphology and the spatial disposition of these three aggregates are hypothesised to be correlated to the advancement of the disease. In order to establish a behavioural disease model related to the Tau proteins aggregates, it is necessary to develop algorithms to detect and segment them automatically. We present a 5-folded pipeline aiming to perform with clinically operational results. This pipeline is composed of a non-linear colour normalisation, a CNN-based image classifier, an Unet-based image segmentation stage, and a morphological analysis of the segmented objects. The tangle detection and segmentation algorithms improve state-of-the-art performances (75.8% and 91.1% F1- score, respectively), and create a reference for neuritic plaques detection and segmentation (81.3% and 78.2% F1-score, respectively). These results constitute an initial baseline in an area where no prior results exist, as far as we know. The pipeline is complete and based on a promising state-of-the-art architecture. Therefore, we consider this study a handy baseline of an impactful extension to support new advances in Alzheimer’s disease. Moreover, building a fully operational pipeline will be crucial to create a 3D histology map for a deeper understanding of clinico-pathological associations in Alzheimer’s disease and the histology-based evidence of disease stratification among different sub-types.
Filippo Fraggetta, Vincenzo L’Imperio, David Ameisen, Rita Carvalho, Sabine Leh, Tim-Rasmus Kiehl, Mircea Serbanescu, Daniel Racoceanu, Vincenzo Della Mea, Antonio Polonia,et al.
MDPI AG
The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.
Ryad Zemouri and Daniel Racoceanu
Springer International Publishing
Fedra Trujillano, Jessenia Gonzalez, Carlos Saito, Andres Flores, and Daniel Racoceanu
IEEE
Corn is cultivated by smallholder farmers in Ancash - Peru and it is one of the most important crops of the region. Climate change and migration from rural to urban areas are affecting agricultural production and therefore, food security. Information about the cultivated extension is needed for the authorities in order to evaluate the impact in the region. The present study proposes corn areas segmentation in multi-spectral images acquired from Unmanned Aerial Vehicles (UAV), using convolutional neural networks. U-net and U-net using VGG11 encoder were compared using dice and IoU coefficient as metrics. Results show that with the second model, 81.5% dice coefficient can be obtained in this challenging task, allowing envisaging an effective and efficient use of this technology, in this hard context.
Ryad Zemouri, Noureddine Zerhouni, and Daniel Racoceanu
MDPI AG
Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.
Jean-Rémi Lapaire
De Boeck Supérieur
Gabriel Jiménez and Daniel Racoceanu
Frontiers Media SA
Existing computational approaches have not yet resulted in effective and efficient computer-aided tools that are used in pathologists' daily practice. Focusing on a computer-based qualification for breast cancer diagnosis, the present study proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consists of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing, with an F1-score of 94.35%. This result is higher than the results using classical image processing techniques and also higher than the approaches combining CCNs with handcrafted features. The second approach is an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6, higher than the existing results using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results show the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last sections; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the proposed technology.
Fedra Trujillano, Andres Flores, Carlos Saito, Mario Balcazar, and Daniel Racoceanu
IEEE
Climate change is affecting the agricultural production in Ancash - Peru and corn is one of the most important crops of the region. It is essential to constantly monitor grain yields and generate statistic models in order to evaluate how climate change will affect food security. The present study proposes as a proof of concept to use Deep learning techniques for the classification of near infrared images, acquired by an Unmanned Aerial Vehicle (UAV), in order to estimate areas of corn, for food security purpose. The results show that using a well balanced (altitudes, seasons, regions) database during the acquisition process improves the performance of a trained system, therefore facing crop classification from a variable and difficult-to-access geography.
Monjoy Saha, Chandan Chakraborty, and Daniel Racoceanu
Elsevier BV