Automated tracing of myelinated axons and detection of the nodes of Ranvier in serial images of peripheral nerves

The development of realistic neuroanatomical models of peripheral nerves for simulation purposes requires the reconstruction of the morphology of the myelinated fibres in the nerve, including their nodes of Ranvier. Currently, this information has to be extracted by semimanual procedures, which severely limit the scalability of the experiments. In this contribution, we propose a supervised machine learning approach for the detailed reconstruction of the geometry of fibres inside a peripheral nerve based on its high-resolution serial section images. Learning from sparse expert annotations, the algorithm traces myelinated axons, even across the nodes of Ranvier. The latter are detected automatically. The approach is based on classifying the myelinated membranes in a supervised fashion, closing the membrane gaps by solving an assignment problem, and classifying the closed gaps for the nodes of Ranvier detection. The algorithm has been validated on two very different datasets: (i) rat vagus nerve subvolume, SBFSEM microscope, 200 × 200 × 200 nm resolution, (ii) rat sensory branch subvolume, confocal microscope, 384 × 384 × 800 nm resolution. For the first dataset, the algorithm correctly reconstructed 88% of the axons (241 out of 273) and achieved 92% accuracy on the task of Ranvier node detection. For the second dataset, the gap closing algorithm correctly closed 96.2% of the gaps, and 55% of axons were reconstructed correctly through the whole volume. On both datasets, training the algorithm on a small data subset and applying it to the full dataset takes a fraction of the time required by the currently used semiautomated protocols. Our software, raw data and ground truth annotations are available at http://hci.iwr.uni-heidelberg.de/Benchmarks/. The development version of the code can be found at https://github.com/RWalecki/ATMA

Neuropathy Classification of Corneal Nerve Images Using Artificial Intelligence

Nerve variations in the human cornea have been associated with alterations in the neuropathy state of a patient suffering from chronic diseases. For some diseases, such as diabetes, detection of neuropathy prior to visible symptoms is important, whereas for others, such as multiple sclerosis, early prediction of disease worsening is crucial. As current methods fail to provide early diagnosis of neuropathy, in vivo corneal confocal microscopy enables very early insight into the nerve damage by illuminating and magnifying the human cornea. This non-invasive method captures a sequence of images from the corneal sub-basal nerve plexus. Current practices of manual nerve tracing and classification impede the advancement of medical research in this domain. Since corneal nerve analysis for neuropathy is in its initial stages, there is a dire need for process automation. To address this limitation, we seek to automate the two stages of this process: nerve segmentation and neuropathy classification of images. For nerve segmentation, we compare the performance of two existing solutions on multiple datasets to select the appropriate method and proceed to the classification stage. Consequently, we approach neuropathy classification of the images through artificial intelligence using Adaptive Neuro-Fuzzy Inference System, Support Vector Machines, Naïve Bayes and k-nearest neighbors. We further compare the performance of machine learning classifiers with deep learning. We ascertained that nerve segmentation using convolutional neural networks provided a significant improvement in sensitivity and false negative rate by at least 5% over the state-of-the-art software. For classification, ANFIS yielded the best classification accuracy of 93.7% compared to other classifiers. Furthermore, for this problem, machine learning approaches performed better in terms of classification accuracy than deep learning

Algorithms for the automatic tracking of the blood vessels network in retinal images acquired by RetCam in newborns

L’obiettivo di questo lavoro di tesi è la realizzazione di una serie di algoritmi capaci di tracciare automaticamente i vasi retinici in immagini acquisite tramite RetCam (field of view=130°) da neonati prematuri. I neonati prematuri rischiano infatti di sviluppare una patologia (Retinopatia del Prematuro) che se non correttamente trattata può portare al distacco retinico e alla cecità. L’analisi del fondo oculare è l’unico modo per determinare la condizione del paziente ma decidere se intervenire o meno in un neonato dovrebbe essere una decisione basata su dati oggettivi e su un protocollo ben definito. Il tracciamento automatico dei vasi retinici in immagini RetCam è un processo complicato data la scarsa qualità delle immagini da elaborare, soprattutto per la trasparenza della retina nei neonati e per il basso contrasto delle immagini, ma rappresenta uno step fondamentale per la successiva valutazione automatica della condizione della retina sotto esame. In questo lavoro sono state considerate 20 immagini, di cui è stato realizzato il tracciamento manuale per determinare la performance del sistema implementato. Fra le 20 immagini ne sono state scelte 6 per allenare un classificatore che , a partire dalle immagini filtrate, distingueva ogni segmento dell’immagine come appartenente o meno alla rete di vasi retinici. il risultato finale è dato dalla combinazione delle 2 classificazioni disponibili per ogni immagine ed è caratterizzato da un’immagine binaria avente i vasi retinici bianchi su sfondo ner

Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community

Steerable3D: An ImageJ plugin for neurovascular enhancement in 3-D segmentation

PurposeImage processing plays a fundamental role in the study of central nervous system, for example in the analysis of the vascular network in neurodegenerative diseases. Synchrotron X-ray Phase-contrast micro-Tomography (SXPCT) is a very attractive method to study weakly absorbing samples and features, such as the vascular network in the spinal cord (SC). However, the identification and segmentation of vascular structures in SXPCT images is seriously hampered by the presence of image noise and strong contrast inhomogeneities, due to the sensitivity of the technique to small electronic density variations. In order to help with these tasks, we implemented a user-friendly ImageJ plugin based on a 3D Gaussian steerable filter, tuned up for the enhancement of tubular structures in SXPCT images.MethodsThe developed 3D Gaussian steerable filter plugin for ImageJ is based on the steerability properties of Gaussian derivatives. We applied it to SXPCT images of ex-vivo mouse SCs acquired at different experimental conditions.ResultsThe filter response shows a strong amplification of the source image contrast-to-background ratio (CBR), independently of structures orientation. We found that after the filter application, the CBR ratio increases by a factor ranging from ~6 to ~60. In addition, we also observed an increase of 35% of the contrast to noise ratio in the case of injured mouse SC.ConclusionThe developed tool can generally facilitate the detection/segmentation of capillaries, veins and arteries that were not clearly observable in non-filtered SXPCT images. Its systematic application could allow obtaining quantitative information from pre-clinical and clinical images

Contrast-enhanced micro-computed tomography and image processing integrated approach for microstructural analysis of biological soft fibrous tissues

Nel sistema muscolo-scheletrico, tendini e legamenti svolgono un ruolo importante al fine di garantire mobilità e stabilità. Questi tessuti sono composti principalmente da collagene e presentano una struttura altamente fibrosa. Evidenziare i componenti della microstruttura di legamenti e tendini in immagini tridimensionali (3D) è di fondamentale importanza per estrarre informazioni significative che posso anvere ripercussioni sulla scienza di base e sulle applicazioni ortopediche. In particolare, le proprietà meccaniche delle microstrutture fibrose sono fortemente influenzate da alcune caratteristiche geometriche, come la volume fraction, l’orientamento e il diametro; tuttavia, determinare l'orientamento e il diametro della fibra 3D è impegnativo. In questa prospettiva, questa tesi mirava ad unire tomografia microcomputerizzata (microCT) ed elaborazione delle immagini in un approccio integrato al fine di identificare e migliorare le informazioni microstrutturali sui tessuti biologici fibrosi, includendo i dati di volume e orientamento. La procedura complessiva è stata applicata per la prima volta su campioni di tendine del ginocchio umano e su legamento collaterale bovino. In una prima fase, sono state testate preparazioni specifiche del campione, inclusa una disidratazione chimica o soluzioni di acido fosfotungstico (PTA) al 2 % in acqua (H2O) o in soluzione di etanolo al 70% (EtOH), così da migliorare il contrasto dell'immagine di questi specifici tessuti. Inoltre, utilizzando i dati scansionati, è stata sviluppata una nuova tecnica di elaborazione delle immagini basata sul filtro 3D hessiano multiscala per evidenziare le strutture fibrose ed ottenere informazioni quantitative sulle fibre. È interessante notare che, per qualsiasi strategia di preparazione del campione di tendini/legamenti, l'approccio proposto è risultato adeguato per rilevare e caratterizzare le proprietà del fascicolo. I risultati del test hanno mostrato che la disposizione delle fibre è fortemente allineata lungo la direzione longitudinale principale nel tendine del tendine, più delle fibre del legamento collaterale bovino. Inoltre, questa tecnica è stata ulteriormente applicata al fine di determinare come il Legamento Crociato Anteriore (LCA) umano risponda a carichi uniassiali rispetto a valori crescenti di deformazione, considerando sia un tessuto sano che uno in condizioni patologiche, cioè acquisito da un paziente con l'artrosi. Anche in questi casi, l'approccio integrato si è rivelato valido ed affidabile nell'individuare orientamento e dimensione dei fascicoli presenti e, quindi, attraverso un modello meccanico strutturale - basato su specifiche leggi costitutive - nello stimare il modulo elastico di questi tessuti. Sono state infatti stimate le curve sforzo-deformazione, ottenendo un valore di modulo elastico di 60.8 MPa e 7.7 MPa rispettivamente per il LCA sano e patologico. In conclusione, è stato progettato e validato in via preliminare un nuovo protocollo microCT per il miglioramento del contrasto dedicato all'analisi microstrutturale dei tessuti molli biologici con caratteristiche fibrose. In una peculiare applicazione al LCA, le informazioni ottenute con il protocollo sono state utilizzate per implementare un modello meccanico dei tessuti fibrosi, stimando così il comportamento biomeccanico dei tessuti sani e patologici.ABSTRACT In the musculoskeletal system, tendons and ligaments play an important role in ensuring mobility and stability. These tissues are primarily composed of collagen and present a highly fibrous structure. Highlighting the microstructure components of ligaments and tendons in three-dimensional (3D) images is crucial for extracting meaningful information impacting basic science and orthopaedic applications. In particular, the mechanical properties of the fibrous microstructures are strongly influenced by their volume fraction, orientation, and diameter. However, determining the 3D fibre orientation and diameter is challenging. In this picture, this thesis aimed at integrating microcomputed tomography (microCT) and image processing approach to identify and enhance microstructural information about biological soft fibrous tissues, including volume and orientation. The overall procedure was first applied on human hamstring tendon and bovine collateral ligament samples. In a first phase specific sample preparations – including either a chemical dehydration, or by 2% of phosphotungstic acid (PTA) in water (H2O) or in 70% ethanol (EtOH) solution – were tested to enhance image contrast of these specific soft tissues. Further, using the scanned data, a novel image processing technique based on 3D Hessian multiscale filter for highlighting fibrous structures was developed to obtain quantitative fibre information. Interestingly, for any strategy of tendon/ligament sample preparation, the proposed approach was adequate for detecting and characterizing fascicle features. The test results showed the fibre arrangement strongly aligned along the main longitudinal direction in the human hamstring tendon more than fibres on the bovine collateral ligament. Moreover, this technique was further applied in order to determine how the human Anterior Cruciate Ligament (ACL) responds to uniaxial loads with respect to increasing values of strain, considering both a healthy tissue and a one under pathological conditions, i.e., acquired from a patient with osteoarthritis. Also in these cases, the integrated approach was valuable and reliable in identifying orientation and size of present fascicles and, thus, through a structural mechanical model - based on specific constitutive law - to estimate the elastic modulus of these tissues. In fact, stress-strain curves were estimated, obtaining a value of elastic modulus of 60.8 MPa and 7.7 MPa for the healthy and pathological ACLs, respectively. In conclusion, a novel contrast enhancement microCT protocol was designed and preliminarily validated for the microstructural analysis of biological soft fibrous tissues. In a peculiar application to ACL, the information obtained with the protocol was used to implement a mechanical model of fibrous tissues, thus estimating the biomechanical behaviour of the healthy and pathological tissues

Towards development of automatic path planning system in image-guided neurosurgery

With the advent of advanced computer technology, many computer-aided systems have evolved to assist in medical related work including treatment, diagnosis, and even surgery. In modern neurosurgery, Magnetic Resonance Image guided stereotactic surgery exactly complies with this trend. It is a minimally invasive operation being much safer than the traditional open-skull surgery, and offers higher precision and more effective operating procedures compared to conventional craniotomy. However, such operations still face significant challenges of planning the optimal neurosurgical path in order to reach the ideal position without damage to important internal structures. This research aims to address this major challenge. The work begins with an investigation of the problem of distortion induced by MR images. It then goes on to build a template of the Circle of Wills brain vessels, realized from a collection of Magnetic Resonance Angiography images, which is needed to maintain operating standards when, as in many cases, Magnetic Resonance Angiography images are not available for patients. Demographic data of brain tumours are also studied to obtain further understanding of diseased human brains through the development of an effect classifier. The developed system allows the internal brain structure to be ‘seen’ clearly before the surgery, giving surgeons a clear picture and thereby makes a significant contribution to the eventual development of a fully automatic path planning system