A methodology for peripheral nerve segmentation using a multiple annotators approach based on Centered Kernel Alignment

Peripheral Nerve Blocking (PNB) is a technique commonly used to perform regional anesthesia and for pain management. The success of PNB procedures depends on the accurate location of the target nerve. Recently, ultrasound imaging has been widely used to locate nerve structures to carry out PNB, due to it enables a non-invasive visualization of the target nerve and the anatomical structures around it. However, the ultrasound images are affected by several artifacts making difficult the accurate delimitation of nerves. In the literature, several approaches have been proposed to carry out automatic or semi-automatic segmentation. Nevertheless, these methods are designed assuming that the gold standard is available, and for this segmentation problem this gold standard can not be obtained considering that it corresponds to subjective interpretation. In this sense, for building those segmentation models, we do not have access to the actual label but an amount of subjective annotations provided by multiple experts. To deal with this drawback we use the concepts of a relatively new area of machine learning known as “Learning from crowds”, this area deals with supervised learning problems considering the case when the gold standard is not available. In this project, we develop a nerve segmentation system that includes: a preprocessing stage, feature extraction methodology based on adaptive methods, and a Centered Kernel Alignment (CKA) based representation to measure the annotators performance for building a classifier with multiple annotators in order to support peripheral nerve segmentation. Our approach to classification with multiple annotators based on CKA is tested on both simulated data and real data; similarly, the methodology of automatic segmentation proposed in this work was tested over ultrasound images labeled by a set of specialists who give their opinion about the location of nerve structures. According to the results, we conclude that our methodology can be used to locate nerve structures in ultrasound images even if the gold standard (the actual location of nerve structures) is not available in the training stage. Moreover, we determine that the approach proposed in this work could be implemented as a guiding tool for the anesthesiologist to carry out PNB procedures assisted by ultrasound imaging

A methodology for peripheral nerve segmentation using a multiple annotators approach based on Centered Kernel Alignment

Peripheral Nerve Blocking (PNB) is a technique commonly used to perform regional anesthesia and for pain management. The success of PNB procedures depends on the accurate location of the target nerve. Recently, ultrasound imaging has been widely used to locate nerve structures to carry out PNB, due to it enables a non-invasive visualization of the target nerve and the anatomical structures around it. However, the ultrasound images are affected by several artifacts making difficult the accurate delimitation of nerves. In the literature, several approaches have been proposed to carry out automatic or semi-automatic segmentation. Nevertheless, these methods are designed assuming that the gold standard is available, and for this segmentation problem this gold standard can not be obtained considering that it corresponds to subjective interpretation. In this sense, for building those segmentation models, we do not have access to the actual label but an amount of subjective annotations provided by multiple experts. To deal with this drawback we use the concepts of a relatively new area of machine learning known as “Learning from crowds”, this area deals with supervised learning problems considering the case when the gold standard is not available. In this project, we develop a nerve segmentation system that includes: a preprocessing stage, feature extraction methodology based on adaptive methods, and a Centered Kernel Alignment (CKA) based representation to measure the annotators performance for building a classifier with multiple annotators in order to support peripheral nerve segmentation. Our approach to classification with multiple annotators based on CKA is tested on both simulated data and real data; similarly, the methodology of automatic segmentation proposed in this work was tested over ultrasound images labeled by a set of specialists who give their opinion about the location of nerve structures. According to the results, we conclude that our methodology can be used to locate nerve structures in ultrasound images even if the gold standard (the actual location of nerve structures) is not available in the training stage. Moreover, we determine that the approach proposed in this work could be implemented as a guiding tool for the anesthesiologist to carry out PNB procedures assisted by ultrasound imaging

Enhancement of nerve structure segmentation by a correntropy-based pre-image approach

El bloqueo de nervios periféricos (PNB) es una técnica ampliamente usada para llevar a cabo anestesia regional en el manejo del dolor. El PNB aplica una sustancia anestésica en el área que rodea el nervio que se quiere intervenir, y su éxito depende de la localización exacta del mismo. Recientemente, las imágenes de ultrasonido (UI) se han utilizado para la localización de nervios periféricos en PNB ya que permiten una visualización no invasiva y directa del nervio y de las estructuras anatómicas alrededor de él; sin embargo, este tipo de imágenes están afectadas por ruido speckle, dificultando su delimitación exacta. De esta manera, es pertinente una etapa de filtrado para atenuar el ruido sin remover información anatómica importante para la segmentación. En este artículo se propone una estrategia para el mejoramiento de UI usando filtrado basado en pre-imágenes. En particular, las imágenes se mapean a un espacio de alta dimensionalidad a través de una función kernel. Específicamente, se emplea un mapeo basado en Correntropía con el fin de codificar estadísticos de orden superior de las imágenes bajo condiciones no-lineales y no-Gaussianas. El enfoque propuesto se valida en la segmentación de nervios para PNB. El enfoque de filtrado basado en pre-imágenes con Correntropía (CPIF) es usado como pre-procesamiento en tareas de segmentación de nervios sobre UI. El rendimiento de la segmentación es medida en términos del coeficiente Dice. De acuerdo con los resultados, CPIF encuentra una aproximación adecuada para las UI al asegurar la identificación de patrones discriminativos de estructuras nerviosas.Peripheral Nerve Blocking (PNB) is a commonly used technique for performing regional anesthesia and managing pain. PNB comprises the administration of anesthetics in the proximity of a nerve. In this sense, the success of PNB procedures depends on an accurate location of the target nerve. Recently, ultrasound images (UI) have been widely used to locate nerve structures for PNB, since they enable a non-invasive visualization of the target nerve and the anatomical structures around it. However, UI are affected by speckle noise, which makes it difficult to accurately locate a given nerve. Thus, it is necessary to perform a filtering step to attenuate the speckle noise without eliminating relevant anatomical details that are required for high-level tasks, such as segmentation of nerve structures. In this paper, we propose an UI improvement strategy with the use of a pre-image-based filter. In particular, we map the input images by a nonlinear function (kernel). Specifically, we employ a correntropy-based mapping as kernel functional to code higher-order statistics of the input data under both nonlinear and non-Gaussian conditions. We validate our approach against an UI dataset focused on nerve segmentation for PNB. Likewise, our Correntropy-based Pre-Image Filtering (CPIF) is applied as a pre-processing stage to segment nerve structures in a UI. The segmentation performance is measured in terms of the Dice coefficient. According to the results, we observe that CPIF finds a suitable approximation for UI by highlighting discriminative nerve patterns

Ultrasound imaging system combined with multi-modality image analysis algorithms to monitor changes in anatomical structures

This dissertation concerns the development and validation of an ultrasound imaging system and novel image analysis algorithms applicable to multiple imaging modalities. The ultrasound imaging system will include a framework for 3D volume reconstruction of freehand ultrasound: a mechanism to register the 3D volumes across time and subjects, as well as with other imaging modalities, and a playback mechanism to view image slices concurrently from different acquisitions that correspond to the same anatomical region. The novel image analysis algorithms include a noise reduction method that clusters pixels into homogenous patches using a directed graph of edges between neighboring pixels, a segmentation method that creates a hierarchical graph structure using statistical analysis and a voting system to determine the similarity between homogeneous patches given their neighborhood, and finally, a hybrid atlas-based registration method that makes use of intensity corrections induced at anatomical landmarks to regulate deformable registration. The combination of the ultrasound imaging system and the image analysis algorithms will provide the ability to monitor nerve regeneration in patients undergoing regenerative, repair or transplant strategies in a sequential, non-invasive manner, including visualization of registered real-time and pre-acquired data, thus enabling preventive and therapeutic strategies for nerve regeneration in Composite Tissue Allotransplantation (CTA). The registration algorithm is also applied to MR images of the brain to obtain reliable and efficient segmentation of the hippocampus, which is a prominent structure in the study of diseases of the elderly such as vascular dementia, Alzheimer’s, and late life depression. Experimental results on 2D and 3D images, including simulated and real images, with illustrations visualizing the intermediate outcomes and the final results are presented.

Identificación automática de nervios periféricos usando técnicas de aprendizaje automático y modelos de forma y apariencia

En este trabajo se presenta una herramienta para la segmentación automática de estructuras nerviosas en imágenes de ultrasonido, la cual tiene como finalidad servir como asistencia para los anestesiólogos en procedimientos de bloqueo de nervios periféricos o PNB por sus siglas en inglés (Peripheral Nerve Blocking). La idea principal de este trabajo es automatizar un modelo de forma y apariencia, el cual requiere de la inicialización por parte de un experto. Esta automatización se lleva a cabo a partir de un modelo de clasificación basado en máquinas de soporte vectorial (SVM), el cual define de manera automática una región de interés (ROI) donde encuentra una estructura nerviosa. Esta ROI es posteriormente usada para la inicialización del modelo de forma y apariencia nombrado anteriormente. La metodología propuesta es probada sobre una base de datos compuesta por imágenes de ultrasonido correspondientes a los nervios cubital y mediano. Los resultados obtenidos comprueban que la metodología propuesta permite de manera automática identificar estructuras nerviosas en imágenes de ultrasonido

Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines

Background Artificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack of knowledge transfer between disciplines, leading to repetition and diverging methodologies. This scoping review aimed to identify and map the available literature on the accuracy and utility of AI systems for ultrasound scanning in regional anaesthesia. Methods A literature search was conducted using Medline, Embase, CINAHL, IEEE Xplore, and ACM Digital Library. Clinical trial registries, a registry of doctoral theses, regulatory authority databases, and websites of learned societies in the field were searched. Online commercial sources were also reviewed. Results In total, 13,014 sources were identified; 116 were included for full-text review. A marked change in AI techniques was noted in 2016–17, from which point on the predominant technique used was deep learning. Methods of evaluating accuracy are variable, meaning it is impossible to compare the performance of one model with another. Evaluations of utility are more comparable, but predominantly gained from the simulation setting with limited clinical data on efficacy or safety. Study methodology and reporting lack standardisation. Conclusions There is a lack of structure to the evaluation of accuracy and utility of AI for ultrasound scanning in regional anaesthesia, which hinders rigorous appraisal and clinical uptake. A framework for consistent evaluation is needed to inform model evaluation, allow comparison between approaches/models, and facilitate appropriate clinical adoption

Computational models for stuctural analysis of retinal images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe evaluation of retina structures has been of great interest because it could be used as a non-intrusive diagnosis in modern ophthalmology to detect many important eye diseases as well as cardiovascular disorders. A variety of retinal image analysis tools have been developed to assist ophthalmologists and eye diseases experts by reducing the time required in eye screening, optimising the costs as well as providing efficient disease treatment and management systems. A key component in these tools is the segmentation and quantification of retina structures. However, the imaging artefacts such as noise, intensity homogeneity and the overlapping tissue of retina structures can cause significant degradations to the performance of these automated image analysis tools. This thesis aims to provide robust and reliable automated retinal image analysis technique to allow for early detection of various retinal and other diseases. In particular, four innovative segmentation methods have been proposed, including two for retinal vessel network segmentation, two for optic disc segmentation and one for retina nerve fibre layers detection. First, three pre-processing operations are combined in the segmentation method to remove noise and enhance the appearance of the blood vessel in the image, and a Mixture of Gaussians is used to extract the blood vessel tree. Second, a graph cut segmentation approach is introduced, which incorporates the mechanism of vectors flux into the graph formulation to allow for the segmentation of very narrow blood vessels. Third, the optic disc segmentation is performed using two alternative methods: the Markov random field image reconstruction approach detects the optic disc by removing the blood vessels from the optic disc area, and the graph cut with compensation factor method achieves that using prior information of the blood vessels. Fourth, the boundaries of the retinal nerve fibre layer (RNFL) are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. The strong experimental results of our retinal blood vessel segmentation methods including Mixture of Gaussian, Graph Cut achieved an average accuracy of 94:33%, 94:27% respectively. Our optic disc segmentation methods including Markov Random Field and Compensation Factor also achieved an average sensitivity of 92:85% and 85:70% respectively. These results obtained on several public datasets and compared with existing methods have shown that our proposed methods are robust and efficient in the segmenting retinal structures such the blood vessels and the optic disc.Brunel University Londonhttp://bura.brunel.ac.uk/bitstream/2438/10387/1/FulltextThesis.pd

Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed