HIGH PERFORMANCE MODELLING AND COMPUTING IN COMPLEX MEDICAL CONDITIONS: REALISTIC CEREBELLUM SIMULATION AND REAL-TIME BRAIN CANCER DETECTION

The personalized medicine is the medicine of the future. This innovation is supported by the ongoing technological development that will be crucial in this field. Several areas in the healthcare research require performant technological systems, which elaborate huge amount of data in real-time. By exploiting the High Performance Computing technologies, scientists want to reach the goal of developing accurate diagnosis and personalized therapies. To reach these goals three main activities have to be investigated: managing a great amount of data acquisition and analysis, designing computational models to simulate the patient clinical status, and developing medical support systems to provide fast decisions during diagnosis or therapies. These three aspects are strongly supported by technological systems that could appear disconnected. However, in this new medicine, they will be in some way connected. As far as the data are concerned, today people are immersed in technology, producing a huge amount of heterogeneous data. Part of these is characterized by a great medical potential that could facilitate the delineation of the patient health condition and could be integrated in our medical record facilitating clinical decisions. To ensure this process technological systems able to organize, analyse and share these information are needed. Furthermore, they should guarantee a fast data usability. In this contest HPC and, in particular, the multicore and manycore processors, will surely have a high importance since they are capable to spread the computational workload on different cores to reduce the elaboration times. These solutions are crucial also in the computational modelling, field where several research groups aim to implement models able to realistically reproduce the human organs behaviour to develop their simulators. They are called digital twins and allow to reproduce the organ activity of a specific patient to study the disease progression or a new therapy. Patient data will be the inputs of these models which will predict her/his condition, avoiding invasive and expensive exams. The technological support that a realistic organ simulator requires is significant from the computational point of view. For this reason, devices as GPUs, FPGAs, multicore devices or even supercomputers are needed. As an example in this field, the development of a cerebellar simulator exploiting HPC will be described in the second chapter of this work. The complexity of the realistic mathematical models used will justify such a technological choice to reach reduced elaboration times. This work is within the Human Brain Project that aims to run a complete realistic simulation of the human brain. Finally, these technologies have a crucial role in the medical support system development. Most of the times during surgeries, it is very important that a support system provides a real-time answer. Moreover, the fact that this answer is the result of the elaboration of a complex mathematical problem, makes HPC system essential also in this field. If environments such as surgeries are considered, it is more plausible that the computation is performed by local desktop systems able to elaborate the data directly acquired during the surgery. The third chapter of this thesis describes the development of a brain cancer detection system, exploiting GPUs. This support system, developed as part of the HELICoiD project, performs a real-time elaboration of the brain hyperspectral images, acquired during surgery, to provide a classification map which highlights the tumor. The neurosurgeon is facilitated in the tissue resection. In this field, the GPU has been crucial to provide a real-time elaboration. Finally, it is possible to assert that in most of the fields of the personalized medicine, HPC will have a crucial role since they consist in the elaboration of a great amount of data in reduced times, aiming to provide specific diagnosis and therapies for the patient

Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection

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Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection

The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of the European project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main goal the application of hyperspectral imaging to the delineation of brain tumors in real-time during neurosurgical operations. In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented. Data was acquired employing a customized hyperspectral acquisition system capable of capturing information in the Visual and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed for the cases where two images of the same scene were captured consecutively. The analysis reveals that the system works more efficiently in the spectral range between 450 and 900 nm. A total of 36 hyperspectral images from 22 different patients were obtained. From these data, more than 300 000 spectral signatures were labeled employing a semi-automatic methodology based on the spectral angle mapper algorithm. Four different classes were defined: normal tissue, tumor tissue, blood vessel, and background elements. All the hyperspectral data has been made available in a public repository

Development of deep learning methods for head and neck cancer detection in hyperspectral imaging and digital pathology for surgical guidance

Surgeons performing routine cancer resections utilize palpation and visual inspection, along with time-consuming microscopic tissue analysis, to ensure removal of cancer. Despite this, inadequate surgical cancer margins are reported for up to 10-20% of head and neck squamous cell carcinoma (SCC) operations. There exists a need for surgical guidance with optical imaging to ensure complete cancer resection in the operating room. The objective of this dissertation is to evaluate hyperspectral imaging (HSI) as a non-contact, label-free optical imaging modality to provide intraoperative diagnostic information. For comparison of different optical methods, autofluorescence, RGB composite images synthesized from HSI, and two fluorescent dyes are also acquired and investigated for head and neck cancer detection. A novel and comprehensive dataset was obtained of 585 excised tissue specimens from 204 patients undergoing routine head and neck cancer surgeries. The first aim was to use SCC tissue specimens to determine the potential of HSI for surgical guidance in the challenging task of head and neck SCC detection. It is hypothesized that HSI could reduce time and provide quantitative cancer predictions. State-of-the-art deep learning algorithms were developed for SCC detection in 102 patients and compared to other optical methods. HSI detected SCC with a median AUC score of 85%, and several anatomical locations demonstrated good SCC detection, such as the larynx, oropharynx, hypopharynx, and nasal cavity. To understand the ability of HSI for SCC detection, the most important spectral features were calculated and correlated with known cancer physiology signals, notably oxygenated and deoxygenated hemoglobin. The second aim was to evaluate HSI for tumor detection in thyroid and salivary glands, and RGB images were synthesized using the spectral response curves of the human eye for comparison. Using deep learning, HSI detected thyroid tumors with 86% average AUC score, which outperformed fluorescent dyes and autofluorescence, but HSI-synthesized RGB imagery performed with 90% AUC score. The last aim was to develop deep learning algorithms for head and neck cancer detection in hundreds of digitized histology slides. Slides containing SCC or thyroid carcinoma can be distinguished from normal slides with 94% and 99% AUC scores, respectively, and SCC and thyroid carcinoma can be localized within whole-slide images with 92% and 95% AUC scores, respectively. In conclusion, the outcomes of this thesis work demonstrate that HSI and deep learning methods could aid surgeons and pathologists in detecting head and neck cancers.Ph.D

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

Machine Learning in Image Analysis and Pattern Recognition

This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition