Computational ultrasound tissue characterisation for brain tumour resection

In brain tumour resection, it is vital to know where critical neurovascular structuresand tumours are located to minimise surgical injuries and cancer recurrence. Theaim of this thesis was to improve intraoperative guidance during brain tumourresection by integrating both ultrasound standard imaging and elastography in thesurgical workflow. Brain tumour resection requires surgeons to identify the tumourboundaries to preserve healthy brain tissue and prevent cancer recurrence. Thisthesis proposes to use ultrasound elastography in combination with conventionalultrasound B-mode imaging to better characterise tumour tissue during surgery.Ultrasound elastography comprises a set of techniques that measure tissue stiffness,which is a known biomarker of brain tumours. The objectives of the researchreported in this thesis are to implement novel learning-based methods for ultrasoundelastography and to integrate them in an image-guided intervention framework.Accurate and real-time intraoperative estimation of tissue elasticity can guide towardsbetter delineation of brain tumours and improve the outcome of neurosurgery. We firstinvestigated current challenges in quasi-static elastography, which evaluates tissuedeformation (strain) by estimating the displacement between successive ultrasoundframes, acquired before and after applying manual compression. Recent approachesin ultrasound elastography have demonstrated that convolutional neural networkscan capture ultrasound high-frequency content and produce accurate strain estimates.We proposed a new unsupervised deep learning method for strain prediction, wherethe training of the network is driven by a regularised cost function, composed of asimilarity metric and a regularisation term that preserves displacement continuityby directly optimising the strain smoothness. We further improved the accuracy of our method by proposing a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. We then demonstrateinitial results towards extending our ultrasound displacement estimation method toshear wave elastography, which provides a quantitative estimation of tissue stiffness.Furthermore, this thesis describes the development of an open-source image-guidedintervention platform, specifically designed to combine intra-operative ultrasoundimaging with a neuronavigation system and perform real-time ultrasound tissuecharacterisation. The integration was conducted using commercial hardware andvalidated on an anatomical phantom. Finally, preliminary results on the feasibilityand safety of the use of a novel intraoperative ultrasound probe designed for pituitarysurgery are presented. Prior to the clinical assessment of our image-guided platform,the ability of the ultrasound probe to be used alongside standard surgical equipmentwas demonstrated in 5 pituitary cases

An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation

Quasi-static ultrasound elastography (USE) is an imaging modality that consists of determining a measure of deformation (i.e.strain) of soft tissue in response to an applied mechanical force. The strain is generally determined by estimating the displacement between successive ultrasound frames acquired before and after applying manual compression. The computational efficiency and accuracy of the displacement prediction, also known as time-delay estimation, are key challenges for real-time USE applications. In this paper, we present a novel deep-learning method for efficient time-delay estimation between ultrasound radio-frequency (RF) data. The proposed method consists of a convolutional neural network (CNN) that predicts a displacement field between a pair of pre- and post-compression ultrasound RF frames. The network is trained in an unsupervised way, by optimizing a similarity metric be-tween the reference and compressed image. We also introduce a new regularization term that preserves displacement continuity by directly optimizing the strain smoothness. We validated the performance of our method by using both ultrasound simulation and in vivo data on healthy volunteers. We also compared the performance of our method with a state-of-the-art method called OVERWIND [17]. Average contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of our method in 30 simulation and 3 in vivo image pairs are 7.70 and 6.95, 7 and 0.31, respectively. Our results suggest that our approach can effectively predict accurate strain images. The unsupervised aspect of our approach represents a great potential for the use of deep learning application for the analysis of clinical ultrasound data.Comment: Accepted at MICCAI 202

DeepReg: a deep learning toolkit for medical image registration

DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.Comment: Accepted in The Journal of Open Source Software (JOSS

First Detection of Leishmania major DNA in Sergentomyia (Spelaeomyia) darlingi from Cutaneous Leishmaniasis Foci in Mali

Leishmania major complex is the main causative agent of zoonotic cutaneous leishmaniasis (ZCL) in the Old World. Phlebotomus papatasi and Phlebotomus duboscqi are recognized vectors of L. major complex in Northern and Southern Sahara, respectively. In Mali, ZCL due to L. major is an emerging public health problem, with several cases reported from different parts of the country. The main objective of the present study was to identify the vectors of Leishmania major in the Bandiagara area, in Mali. Methodology/Principal Findings: An entomological survey was carried out in the ZCL foci of Bandiagara area. Sandflies were collected using CDC miniature light traps and sticky papers. In the field, live female Phlebotomine sandflies were identified and examined for the presence of promastigotes. The remaining sandflies were identified morphologically and tested for Leishmania by PCR in the ITS2 gene. The source of blood meal of the engorged females was determined using the cyt-b sequence. Out of the 3,259 collected sandflies, 1,324 were identified morphologically, and consisted of 20 species, of which four belonged to the genus Phlebotomus and 16 to the genus Sergentomyia. Leishmania major DNA was detected by PCR in 7 of the 446 females (1.6%), specifically 2 out of 115 Phlebotomus duboscqi specimens, and 5 from 198 Sergentomyia darlingi specimens. Human DNA was detected in one blood-fed female S. darlingi positive for L. major DNA. Conclusion: Our data suggest the possible involvement of P. duboscqi and potentially S. darlingi in the transmission of ZCL in Mali

Les équidés peuvent-ils s'adapter aux conditions d’hébergement inhérentes au confinement A3 ?

National audienceGiven the possible re-emergence of equine pathologies (West Nile Fever, African horse sickness) and the need to study these pathologies using in vivo infections, it is necessary to have structures where horses could be housed in BSL-3 in the respect of welfare and ethics rules. To estimate this possibility, 3 pony mares were housed in BSL-3 facilities at the Plate Forme d'Infectiologie Experimentale in INRA (PFIE) to observe their behavior in these conditions of confinement. The observation of these animals did not reveal deviant behaviors for the three females during the test. This validation is of major importance for the scientific community and the equine sector by offering perspectives for the study of equine pathologies, in particular for the arbovirus such as the African Horse sickness or encephalitis.Face à la possible réémergence des pathologies équines (West Nile, Peste Equine ... ) et aux besoins d'étudier ces pathologies en réalisant des infections in vivo, il est nécessaire de disposer de structures pouvant accueillir des équidés en confinement A3 (Animalerie sous pression négative permettant l'étude de pathogènes pouvant présenter un risque pour l'environnement et le manipulateur) dans le respect du bien être animal et de l'éthique. Afin d'évaluer cette capacité, 3 ponettes ont été hébergées dans les locaux A3 de la Plate Forme d'Infectiologie Expérimentale de I'INRA (PFIE) afin d'observer leur comportement dans ces conditions de claustration. L'observation de ces animaux n'a pas permis de déceler de comportements déviants chez les trois ponettes pendant la durée du test. Cette validation est d'une importance majeure pour la communauté scientifique et la filière équine en offrant des perspectives importantes pour l'étude des pathologies équines, en particulier pour les arboviroses telle que la peste équine ou les encéphalites

Schmallenberg <em>in vivo</em> à la PFIE

National audienc

Investigation of Bufavirus and Parvovirus 4 in Patients with Gastro-Enteritis from the South-East of France

Bufavirus (BuV) and human parvovirus 4 (PARV4) belong to the Parvoviridae family. We assessed BuV and PARV4 DNA presence by real-time PCR analysis in stool, blood and respiratory samples collected in patients from Marseille and Nice, two large cities in the South-East of France. Bu-V DNA was detected in diarrheic stool samples from 92 patients (3.6% of 2583 patients), particularly men and adults, and patients from the nephrology and the infectious disease departments. Among the patients with a BuV-positive stool sample and for whom at least one blood sample was available (n = 30 patients), BuV DNA was detected also in 3 blood samples. In contrast, BuV DNA was not detected in any of the respiratory samples from 23 patients with BuV-positive stool. BuV detection rate was comparable in stool samples from patients with and without diarrhea. We did not detect PARV4 DNA in any of the stool specimens (n = 2583 patients). Our results suggest that PARV4 fecal–oral transmission is rare or non-existent in the South-East of France while BuV circulates with a relatively high rate in this area

Evaluation of the in utero infection caused by SBV on goats

International audienc