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UNSTRUCTURED The amount of medical video data that has to be securely stored has been growing exponentially. This rapid expansion is mainly caused by the introduction of higher video resolution such as 4K and 8K to medical devices and the growing usage of telemedicine services, along with a general trend toward increasing transparency with respect to medical treatment, resulting in more and more medical procedures being recorded. Such video data, as medical data, must be maintained for many years, resulting in datasets at the exabytes scale that each hospital must be able to store in the future. Currently, hospitals do not have the required information and communications technology infrastructure to handle such large amounts of data in the long run. In this paper, we discuss the challenges and possible solutions to this problem. We propose a generic architecture for a holistic, end-to-end recording and storage platform for hospitals, define crucial components, and identify existing and future solutions to address all parts of the system. This paper focuses mostly on the recording part of the system by introducing the major challenges in the area of bioinformatics, with particular focus on three major areas: video encoding, video quality, and video metadata.

International audience; Gliomas are the most common form of primary brain tumors. Diffuse Low-Grade Gliomas (Grade II gliomas in the World Health Organization classification of 2016), are slow growing tumors. Neuro-oncologists estimate the tumor size, which is the current gold standard in the assessment of response to therapies. However, tumor evolution remains in many cases unpredictable, likely because it relies on other parameters which have not yet been studied. One of these parameters is the tumor morphology, which seems to be very relevant to assess tumor evolution and response to one treatment. To our knowledge, there is no tool that allows to quantify glioma morphology in the spatial dimension, using a single measure. We propose in this work a new tool allowing to quantify glioma morphology, easy to implement and to interpret for Neuro-oncologists. Ours results shows that there could be various morphologies amongst diffuse low-grade gliomas and that we could follow-up the morphology, especially during response to longitudinal treatment. We will discuss the method by comparing our results with previously published works on glioma morphology before presenting our perspectives.

A fast indexing method dedicated to lattice vector quantization (LVQ), called stack indexing (SI), is presented. It is based on the approach previously proposed by Loyer et al. (2003). Our method addresses the problem of enumerating codebook vectors in lattice Z n and is designed for the compression of Generalized Gaussian sources. Indexing is the key point of the lossless stage of LVQ. It usually requires a high number of additions and thus is often critical in terms of execution time. To circumvent this drawback, SI uses pre-computed cardinalities of n -balls in Z n to enumerate lattice vectors in the lexicographical order. The algorithmic complexity of the SI is low and the number of additions performed does not depend on the dynamic range of the source which makes SI well suited for high dynamic sources. Experimental results performed in still image coding using a discrete wavelet transform confirm that the computational cost is significantly decreased compared to the previous approach.

Management of diffuse low-grade glioma (DLGG) relies extensively on tumour volume estimation from MRI datasets. Two methods are currently clinically used to define this volume: the commonly used three-diameters solution and the more rarely used software-based volume reconstruction from the manual segmentations approach. The authors conducted an initial study of inter-practitioners’ variability of software-based manual segmentations on DLGGs MRI datasets. A panel of 13 experts from various specialties and years of experience delineated 12 DLGGs’ MRI scans. A statistical analysis on the segmented tumour volumes and pixels indicated that the individual practitioner, the years of experience and the specialty seem to have no significant impact on the segmentation of DLGGs. This is an interesting result as it had not yet been demonstrated and as it encourages cross-disciplinary collaboration. Their second study was with the three-diameters method, investigating its impact and that of the software-based volume reconstruction from manual segmentations method on tumour volume. They relied on the same dataset and on a participant from the first study. They compared the average of tumour volumes acquired by software reconstruction from manual segmentations method with tumour volumes obtained with the three-diameters method. The authors found that there is no statistically significant difference between the volumes estimated with the two approaches. These results correspond to non-operated and easily delineable DLGGs and are particularly interesting for time-consuming CUBE MRIs. Nonetheless, the three-diameters method has limitations in estimating tumour volumes for resected DLGGs, for which case the software-based manual segmentation method becomes more appropriate.

Ces dernieres annees, le developpement des technologies numeriques a connu un essor considerable dans le domaine de la sante. Dans ce contexte, les images et les videos medicales representent des donnees essentielles dans les parcours de soins. Celles-ci sont aujourd’hui produites massivement et necessitent des traitements pour pouvoir etre transmises, archivees ou servir pour d’autres usages. Ces traitements algorithmiques pouvant causer des degradations plus ou moins importantes, la mesure de la qualite represente un enjeu scientifique et economique majeur du fait de la sensibilite de ces donnees et de leurs applications en sante. Dans cet article, nous decrirons les concepts de qualite « subjective » et de qualite « objective » et nous decrirons certains des outils associes a ces concepts.

International audience; Diffuse low-grade gliomas (DLGG) are brain tumors of young adults. They affect the quality of life of the inflicted patients and, if untreated, they evolve into higher grade tumors where the patient's life is at risk. Therapeutic management of DLGGs includes chemotherapy, and tumor diameter is particularly important for the follow-up of DLGG evolution. In fact, the main clinical basis for deciding whether to continue chemotherapy is tumor diameter growth rate. In order to reliably assist the doctors in selecting the most appropriate time to stop treatment, we propose a novel clinical decision support system. Based on two mathematical models, one linear and one exponential, we are able to predict the evolution of tumor diameter under Temozolomide chemotherapy as a first treatment and thus offer a prognosis on when to end it. We present the results of an implementation of these models on a database of 42 patients from Nancy and Montpellier University Hospitals. In this database, 38 patients followed the linear model and four patients followed the exponential model. From a training data set of a minimal size of five, we are able to predict the next tumor diameter with high accuracy. Thanks to the corresponding prediction interval, it is possible to check if the new observation corresponds to the predicted diameter. If the observed diameter is within the prediction interval, the clinician is notified that the trend is within a normal range. Otherwise, the practitioner is alerted of a significant change in tumor diameter.

The amount of medical video data that has to be securely stored has been growing exponentially. This rapid expansion is mainly caused by the introduction of higher video resolution such as 4K and 8K to medical devices and the growing usage of telemedicine services, along with a general trend toward increasing transparency with respect to medical treatment, resulting in more and more medical procedures being recorded. Such video data, as medical data, must be maintained for many years, resulting in datasets at the exabytes scale that each hospital must be able to store in the future. Currently, hospitals do not have the required information and communications technology infrastructure to handle such large amounts of data in the long run. In this paper, we discuss the challenges and possible solutions to this problem. We propose a generic architecture for a holistic, end-to-end recording and storage platform for hospitals, define crucial components, and identify existing and future solutions to address all parts of the system. This paper focuses mostly on the recording part of the system by introducing the major challenges in the area of bioinformatics, with particular focus on three major areas: video encoding, video quality, and video metadata.

International audience

International audience; Several sources indicate that assessment in a Living Lab is recurrent, in so far as each stage of design is immediately followed by assessment of what has been produced. The ergonomic approach, mentioned as one of the methods of co‐design, involves an evaluation dimension that is in fact iterative in nature. Impact assessment focuses on the solution's effectiveness for the patient, on the system of work or activity, but also more broadly at organizational level and in public healthcare. This healthcare technology assessment should be carried out by a team other than that responsible for the design phase. The new approach to measuring the impact of public policies, published by the Organization for Economic Cooperation and Development offers new pathways for bringing iterative assessment together with global assessment used in healthcare. Evaluating the impact in terms of public policies requires measurements that differ in nature from those of ergonomics, since they lie outside the solution.

To meet doctors' needs to store and share medical data remotely, lossy compression seems, today, to be an appropriate solution to manage the huge amount of these medical data. However, as there is a risk to lose critical medical information, experts' subjective quality reviews should be considered with respect to compression efficiency. In this context, we try to compare the last two video encoding international standards performances, taking into account quality assessment issues. Results show us that HEVC is more efficient than MPEG-4 AVC/H.264, offering up to 54% bit-rate saving comparing to AVC/H.264. Besides, we showed that ENT medical videos can be advantageously encoded in SD instead of Full HD resolution for low bit-rate applications. Finally, by comparison with doctors' perception, the appropriate objective metrics MSE, NQM, SSIM and MSSIM validate the previous results and confirm the superiority of HEVC over MPEG-4 AVC/H.264. They are very promising for telemedicine applications, especially in low bit-rate context.

Présentation Poster; International audience; Objectives. To accurately identify potentially torsadogenic compounds in an earlier stage of drug development, innovative preclinical strategies including label-free impedance and extracellular field potential recordings of stem cell-derived cardiomyocytes have been recently proposed. Unfortunately, they pro-duce high-content signals and size of their data files may exceed 10GB, which prevents any web data transfer for remote data analysis. Therefore, our objec-tive is to compare the performances of several compression algorithms applied to those signals.Methods. The general plan of a lossy compression algorithm consists of three main stages. The first one transforms the signal in a mathematical space where the signal is sparser in order to decorrelate the data and to minimize their amount (one can use for example Discrete Cosine or Fourier Transforms). The se-cond step is the only lossy step of the compression chain. The quantization of the transform coefficients, which can be associated to thresholding discards the non significant coefficients. The last step optimally encodes the quantized coefficients by using an entropy code (e.g. Huffman algorithm, RLE, arithmetic coding…) to exploit residual redundancies of the quantized data. Several combinations of methods mentioned above have been tested and evaluated on two types of one-dimensional signals obtained from impedimetric contractility and extra cellular potential recordings from cardiac myocytes.Results. Results clearly present the ability and reliability of the different methods to compress sensitive data coming from cardiomyocytes with a compression ratio of up to 10:1 while preserving the relevant information content of the recorded data.Conclusion. The proposed method enables biologists to use web-based remote analysis tools for generated cardiomyocyte impedance and field potential data by reducing the size of their files without distortion of their data.

Cet article presente une illustration d’un projet collaboratif europeen finance par le cluster Celtic Plus dans le domaine de la sante, le projet HIPERMED (HIgh PERformance teleMEDicine platform-http://www.hipermed.org/). Ce projet a abouti a la creation d’une plateforme de telemedecine a haute performance. Cette plate-forme integre, dans une seule et meme interface, differents outils et services multimedias destines aussi bien aux professionnels de sante qu’aux patients. Elle permet ainsi entre sites distants, l’echange en temps reel et de facon simultanee de differents flux d’informations parmi lesquels des videos haute definition (provenant aussi bien de cameras d’ambiance que d’instruments chirurgicaux), des images radiologiques (au format DICOM) ou encore des fichiers texte. Le projet a recu l’“Award” d’argent dans la categorie « Excellence » lors du dernier meeting Celtic Plus, qui a eu lieu a Monaco, les 23 et 24 avril 2014.

International audience; Software-based manual segmentation is critical to the supervision of diffuse low-grade glioma patients and to the optimal treatment’s choice. However, manual segmentationbeing time-consuming, it is difficult to include it in the clinicalroutine. An alternative to circumvent the time cost of manualsegmentation could be to share the task among different practitioners, providing it can be reproduced. The goal of our work is to assess diffuse low-grade gliomas’ manual segmentation’s reproducibility on MRI scans, with regard to practitioners, their experience and field of expertise. A panel of 13 experts manually segmented 12 diffuse low-grade glioma clinical MRI datasets using the OSIRIX software. A statistical analysis gave promising results, as the practitioner factor, the medical specialty and the years of experience seem to have no significant impact on the average values of the tumor volume variable.

International audience; Diffuse low-grade gliomas are rare primitive cerebral tumours of adults. These tumors progress continuously over time and then turn to a higher grade of malignancy associated with neurological disability, leading ultimately to death. Tumour size is one of the most important prognostic factors. Thus, it is of great importance to be able to assess the volume of the tumor during the patients’ monitoring.MRI is nowadays the recommended modality to achieve this. Furthermore, if surgery remains the first option for diffuse low-grade gliomas, chemotherapy is increasingly used (before or after a possible surgery). However, crucial and difficult questions remain to be answered: identifying subgroups ofpatients who could benefit from chemotherapy, determining the best time to initiate chemotherapy, defining the duration of chemotherapy and evaluating the optimal time to perform surgery, or otherwise radiotherapy. In this study, we propose to help clinicians in decision-making, by designing new predictivemodels dedicated to the evolution of the diameter of the tumor. Two proposed statistical models (linear and exponential) have been validated on a database of 16 patients whose temozolomide-based chemotherapy lasted between 14 and 32 months, with an average duration of 22.8 months. The selection of the most appropriate model has been achieved with the corrected Akaike’s Information Criterion. The results are very promising, with coefficients of determination varying from 0.79 to 0.97 with an average value of 0.90 for the linear model. This shows it is possible to alert the clinician to a change in the tumor diameter’s dynamics.

International audience; Today, lossy compression is becoming increasingly important for storage and transmission, of great amounts of numerical data, especially in the telemedicine field. However, the loss of information involved by this kind of compression can be considered as risky in particular for medical applications like diagnosis. To minimize this risk a balance should be done between compression efficiency and experts' perceived quality by running subjective quality assessment for compressed medical data. In this study, we address this issue and we deal with this problem by determining H.264 compression bitrate thresholds for full HD otolaryngology medical sequences. We show that this type of videos could be lossy encoded up to a ratio threshold ranging from 100:1 to 270:1 with maintaining practitioners' satisfaction. Besides, objective results showed us, that the use of quality assessment algorithms such as MSE, NIQE, NQM, SSIM, MSSIM and BRISQUE, could be helpful to compute realistic compression ratios and could, thus, validate human perception. These results are finally improved by a preliminary study, in which we used the new video encoding standard HEVC. It seems to be more efficient than H.264 in terms of objective video quality.

In this paper, we present and evaluate a hardware solution for user-driven and packet loss tolerant image compression, especially designed to enable low power image compression and communication over wireless camera sensor networks (WCSNs). The proposed System-on-Chip is intended to be designed as a hardware coprocessor embedded in the camera sensor node. The goal is to relieve the node microcontroller of the image compression tasks and to achieve high-speed and low power image processing. The interest of our solution is twofold. First, compression settings can be changed at runtime (upon reception of a request message sent by an end user or according to the internal state of the camera sensor node). Second, the image compression chain includes a (block of) pixel interleaving scheme which significantly improves the robustness against packet loss in image communication. We discuss in depth the internal hardware architecture of the encoder chip which is planned to reach high performance running in FPGAs and in ASIC circuits. Synthesis results and relevant performance comparisons with related works are presented. We study a low power hardware solution for image compression in wireless camera sensor network. The goal is to relieve the node microcontroller of the image compression tasks and to achieve low-power image processing. We discuss the internal hardware architecture of the proposed encoder circuit and its implementation. We provide relevant comparisons with related solutions.

International audience; The design of a variable rate joint watermarking and compression (JWC) scheme is examined in this paper. The proposed approach, called modulated lattice vector quantization (MLVQ), is based on dither modulation quantization index modulation (DM-QIM) which allows for embedding information while maintaining good coding performance. In the first part, we propose a specific indexing method to make MLVQ with a multidimensional codebook feasible. Furthermore, a quantization parameter estimation was designed to ensure the invertibility of the embedding. The limitations of the compression performance of JWC schemes are studied in the second part. We show the existence of a coding rate lower bound which depends mainly on the codebook characteristics and dramatically decreases coding performance. To circumvent this drawback, two improved MLVQ schemes are proposed. In the first one, called arbitrary MLVQ, the embedding is performed on part of the signal to ensure a low embedding/coding ratio. In the second one, called deadzone MLVQ, the coding efficiency is further improved by maintaining the sparsity of the quantized host signal. It consists in excluding the sparse signal components from the embedding process then thresholding them. These schemes both applying wavelet coding demonstrate their efficiency as variable rate coders.

International audience; Nowadays, lossy compression is becoming increasingly important for storage and transmission, of hunger amounts of numerical date, especially in the medical field. However, the loss of information involved by this kind of compression can be considered as risky in particular for medical applications like diagnosis. To minimize this risk a balance should be done between compression efficiency and experts’ perceived quality by running subjective quality assessment for compressed medical data. In this study, we address this issue and we deal with this problem by determining H264 compression bitrate thresholds for full HD ENT medical sequences. We show that this type of video could be lossy encoded up to a ratio threshold ranging from 100:1 to 270:1 with maintaining practitioners’ satisfaction. Objective results showed us finally, that the use of quality assessment algorithms such as MSE, NIQE, NQM, SSIM, MSSIM and BRISQUE, could be helpful to compute realistic compression ratios and could, thus, validate human perception.

It is admitted that lossy compression can be used in the field of medical images under the control of experts. Lossy compression can offer substantial reduction of the volumes of medical images, being thus an efficient solution for both storage and transmission problem in the medical context. Furthermore, the use of touchpads in medicine has grown and many medical applications on this kind of support is now available. The storage capacity of this kind of terminal is limited, lossy compression represents a good alternative to allow greedy medical applications on such terminals. In this work, we address the problem of quality assessment of MRI scans from an interactive upper limb radiology atlas (Monster Anatomy Upper Limb). The quality assessment protocol is adapted from the International Telecommunication Union recommendations (ITU-R BT-500-11). In this paper, we propose to determine compression thresholds which are acceptable according to the quality required for the proper use of this radiology atlas. We show that this application (using a simple JPEG encoder) has a lossy compression threshold ranging from 13: 1 for the majority of the atlas images up to 27: 1 for the hand images. Finally, several objective image quality assessment algorithms (IQA) are also linked to subjective ratings of the panel of health professionals.

INTRODUCTION: PCV (procarbazine, CCNU, vincristine) chemotherapy was the first association evaluated in diffuse low grade gliomas (DLGGs). Impact of temozolomide and PCV was previously assessed on growth kinetics. Nevertheless, there are no data concerning the growth kinetics of DLGGs after PCV immediately followed by radiotherapy. METHODS: We retrospectively selected patients with histological diagnosis of DLGGs from a french neuro-oncological center (Nancy) who benefited from PCV chemotherapy immediately followed by conventional radiotherapy. Clinical, neuroimaging (kinetics), pathological and molecular data were recorded. RESULTS: Twelve patients with a median age at diagnosis of 37,9 years (range 24 to 56) were selected. The median follow-up was 17,9 years (range 12,6 to 22,31). The median number of PCV cycles was 5 (range 2 to 6). The median time between the end of PCV chemotherapy and the beginning of the radiotherapy was 2,6 months (range 1 to 5). The median dose of radiotherapy was 54 Gy (range 54 to 60). During the follow-up period, 3 patients died and 3 patients presented anaplastic transformation (median time to transformation 12 years, range 11,8 to 14). The median survival was 18 years (range 12,6 to “not reached”). 2 patients developed severe neurocognitive deficits (dementia) and 1 patient presented a postradiation stroke. 10 DLGG patients had IDH1 R132H mutation, 5 patients showed a surexpression of P53 and 5 expressed alpha-internexin. Before treatment combining PCV and radiotherapy, the median spontaneous growth kinetics was 4,03 mm per year (range 2,32 to 11,26). After the combined treatment, the growth kinetics remain negative in 10 patients with a median of -1,72mm/year (range -5,36 to 1,92). The median duration of negative kinetics was 8,8 years (range 0 to 16,6). CONCLUSIONS: Despite this limited series, we reported growth kinetics data on the synergy of PCV and radiotherapy in DLGGs. The prolonged response seems longer than previous data obtained with PCV alone (median of 2,7 years). Specific neurocognitive and quality of life data will be collected.

International audience; Surveillance applications with Wireless Sensor Networks can be strengthened by introducing imaging capability: intrusion detection, situation awareness, search&rescue. As images are usually bigger than scalar data, and a single image needs to be split in many small packets, image transmission is a real challenge for these applications, especially when knowing that the wireless medium in WSN has high throughput limitations and high packet loss rates due to numerous wireless channel errors and contention. Our contribution in this paper is on identifying limitations and bottlenecks of sensor board hardware and 802.15.4 radio to determine the performance level that can be expected when transmitting still images on a multi-hop network. In this paper, we will present experimentations with real sensor boards and radio modules. We will highlight the main sources of delays assuming no flow control nor congestion control to determine the best case performance level. The objective here is to present the potentials and the limitations of image-based wireless sensor networks.

Synthetic Aperture Radar (SAR) is a day/night and all-weather active imaging system. which can record images with a fine spatial resolution independent of the sensor altitude. All these characteristics make the SAR a valuable instrument for Earth observation. However, computer system technology does not currently enable spaceborne processing and storage of the great amount of data required to synthesize an image. Vector quantization is a lossy numerical method for signal compression. This method can be implemented aboard remote systems and is designed to reduce the bit stream of transmission over a noiseless channel. Analysis and prospects for this compression algorithm, applied on raw SAR data, are presented.