Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering

Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of 1H MRSI data after cluster analysis

Laser Based Mid-Infrared Spectroscopic Imaging – Exploring a Novel Method for Application in Cancer Diagnosis

A number of biomedical studies have shown that mid-infrared spectroscopic images can provide both morphological and biochemical information that can be used for the diagnosis of cancer. Whilst this technique has shown great potential it has yet to be employed by the medical profession. By replacing the conventional broadband thermal source employed in modern FTIR spectrometers with high-brightness, broadly tuneable laser based sources (QCLs and OPGs) we aim to solve one of the main obstacles to the transfer of this technology to the medical arena; namely poor signal to noise ratios at high spatial resolutions and short image acquisition times. In this thesis we take the first steps towards developing the optimum experimental configuration, the data processing algorithms and the spectroscopic image contrast and enhancement methods needed to utilise these high intensity laser based sources. We show that a QCL system is better suited to providing numerical absorbance values (biochemical information) than an OPG system primarily due to the QCL pulse stability. We also discuss practical protocols for the application of spectroscopic imaging to cancer diagnosis and present our spectroscopic imaging results from our laser based spectroscopic imaging experiments of oesophageal cancer tissue

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes

Method for coregistration of optical measurements of breast tissue with histopathology : the importance of accounting for tissue deformations

For the validation of optical diagnostic technologies, experimental results need to be benchmarked against the gold standard. Currently, the gold standard for tissue characterization is assessment of hematoxylin and eosin (H&E)-stained sections by a pathologist. When processing tissue into H&E sections, the shape of the tissue deforms with respect to the initial shape when it was optically measured. We demonstrate the importance of accounting for these tissue deformations when correlating optical measurement with routinely acquired histopathology. We propose a method to register the tissue in the H&E sections to the optical measurements, which corrects for these tissue deformations. We compare the registered H&E sections to H&E sections that were registered with an algorithm that does not account for tissue deformations by evaluating both the shape and the composition of the tissue and using microcomputer tomography data as an independent measure. The proposed method, which did account for tissue deformations, was more accurate than the method that did not account for tissue deformations. These results emphasize the need for a registration method that accounts for tissue deformations, such as the method presented in this study, which can aid in validating optical techniques for clinical use. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License

Objective localisation of oral mucosal lesions using optical coherence tomography.

PhDIdentification of the most representative location for biopsy is critical in establishing the definitive diagnosis of oral mucosal lesions. Currently, this process involves visual evaluation of the colour characteristics of tissue aided by topical application of contrast enhancing agents. Although, this approach is widely practiced, it remains limited by its lack of objectivity in identifying and delineating suspicious areas for biopsy. To overcome this drawback there is a need to introduce a technique that would provide macroscopic guidance based on microscopic imaging and analysis. Optical Coherence Tomography is an emerging high resolution biomedical imaging modality that can potentially be used as an in vivo tool for selection of the most appropriate site for biopsy. This thesis investigates the use of OCT for qualitative and quantitative mapping of oral mucosal lesions. Feasibility studies were performed on patient biopsy samples prior to histopathological processing using a commercial OCT microscope. Qualitative imaging results examining a variety of normal, benign, inflammatory and premalignant lesions of the oral mucosa will be presented. Furthermore, the identification and utilisation of a common quantifiable parameter in OCT and histology of images of normal and dysplastic oral epithelium will be explored thus ensuring objective and reproducible mapping of the progression of oral carcinogenesis. Finally, the selection of the most representative biopsy site of oral epithelial dysplasia would be investigated using a novel approach, scattering attenuation microscopy. It is hoped this approach may help convey more clinical meaning than the conventional visualisation of OCT images

Primary Choroidal Lymphoma Diagnosed with 27-Gauge Pars Plana Vitrectomy Choroidal Biopsy

Currently, transvitreal fine-needle aspiration biopsy is the most widely used tissue biopsy technique in cases of suspected intraocular lymphoma due to its relative simplicity and low trauma. The small sample produced, however, may be inadequate for diagnostic and prognostic analyses due to mechanical artefacts, insufficient material, or sampling errors. Small case series have demonstrated choroidal biopsy via vitrectomy to be safe and effective. With smaller-gauge vitrectomy instruments, visual recovery is rapid, and post-operative inflammation and conjunctival scarring is minimised. Furthermore, smaller-gauge instrumentation does not appear to affect the diagnostic yield of biopsies for intraocular lymphoma in vitro. We report a case of primary choroidal lymphoma successfully diagnosed with 27-gauge pars plana vitrectomy choroidal biopsy. Case Presentation: A 72-year-old female presented with a 6-month history of painless blurred vision in her right eye. Fundus examination revealed a large pale choroidal mass centred on the posterior pole with overlying exudative retinal detachment. Enhanced depth imaging optical coherence tomography revealed a markedly thickened choroid with an undulating appearance. B-scan ultrasonography demonstrated diffuse, smooth thickening of the choroid, and retrobulbar extrascleral hypoechoic nodules. A 27-gauge pars plana vitrectomy was performed and choroidal biopsy taken. Histopathologic, immunohistochemical, and flow cytometry studies confirmed a diagnosis of extranodal marginal zone B-cell lymphoma. Systemic workup found no evidence of systemic lymphoma. As such, the patient was diagnosed with primary choroidal lymphoma. She underwent intensity-modulated external beam radiotherapy with subsequent resolution of disease. Conclusions: Primary choroidal lymphoma can be safely and effectively diagnosed via 27-gauge vitrectomy choroidal biopsy

Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography

Abstract. Osteoarthritis (OA) is a joint disease affecting hundreds of millions of people worldwide. In basic research, accurate ex vivo measures are needed for assessing OA severity. The standard method for this is the histopathological grading of stained thin tissue sections. However, the methods are destructive, time-consuming, do not describe the full sample volume and provide subjective results. Contrast-enhanced micro-computed tomography (CEμCT) -based grading with phosphotungstic acid -stain was previously developed to address some of these issues. Aim of this study was to investigate the possibility of automating this process. Osteochondral tissue cores were harvested from total knee arthroplasty patients (n = 34, N = 19, Ø = 2 mm, n = 15, N = 5, Ø = 4 mm) and asymptomatic cadavers (n = 30, N = 2, Ø = 4 mm). Samples were imaged with CEμCT, reconstructed and graded manually. Subsequently, the reconstructions were loaded into an ad hoc developed Python software, where volumes-of-interest (VOI) were extracted from different cartilage zones: surface zone (SZ), deep zone (DZ) and calcified zone (CZ) and collapsed into two-dimensional texture images. Normalized images underwent Median Robust Extended Local Binary Pattern (MRELBP) -algorithm to extract the features, with subsequent dimensionality reduction. Ridge and logistic regression models were trained with L2 regularization against the ground truth for the small samples (Ø = 2 mm) using leave-one-patient-out cross-validation. Trained models were then evaluated on the large samples (Ø = 4 mm). Performance of the models were assessed using Spearman’s correlation, Area under the Receiver Operating Characteristic Curve (AUC) and Average Precision (AP). Highest performance on both models was for the SZ. Strong correlation was observed on ridge regression (ρ = 0.68, p < 0.0001), as well as high AUC and AP values for the logistic regression (AUC = 0.92, AP = 0.89) for the small samples. Using the large samples, similar findings were observed with slightly reduced values (ρ = 0.55, p = 0.0001, AUC = 0.86, AP = 0.89). Moderate results were observed for CZ and DZ models (ρ = 0.54 and 0.38, AUC = 0.77 and 0.72, AP = 0.71 and 0.50, respectively). Evaluation on the large samples resulted in performance decrease on CZ models (ρ = 0.29, AUC = 0.63, AP = 0.62), while surprisingly performance increased on DZ logistic regression model (ρ = 0.34, AUC = 0.72, AP = 0.83). Obtained results indicate that automating the 3D CEμCT histopathological grading is feasible. However, with low number of samples, models are better suited for binary detection of sample degenerative features, rather than predicting a detailed grade. To facilitate model generalization on new data, similar data acquisition protocol should be used on all samples. The proposed methods have potential to aid OA researchers and pathologists in 3D histopathological grading, introducing more objectivity to the grading process. This thesis presents the conducted study in detail, and provides an extensive review related to the osteochondral unit, CEμCT imaging, as well as statistical learning machines

Generative deep learning in digital pathology workflows

Funding: Supported by the Sir James Mackenzie Institute for Early Diagnosis, University of St Andrews and Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (grant number TS/S013121/1).Many modern histopathology laboratories are in the process of digitising their workflows. Once images of the tissue exist as digital data, it becomes feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on Deep Learning, promise systems that can identify pathologies in slide images with a high degree of accuracy. Generative modelling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology including the removal of color and intensity artefacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses some future directions for generative models within histopathology.PostprintPeer reviewe

Cuidado especial para las muestras de tejido bucal después de la biopsia: Almacenamiento y transporte adecuados — Un estudio comparativo.

Objective: Biopsy is the gold standard for the diagnosis of oral lesions. Correct management and care of biopsy at all steps (before, during and after obtaining a biopsy) is highly important to provide proper tissue samples for microscopic assessment by pathologists. This study aimed to assess and compare the knowledge of dental students (DSs) and general dentists (GDs) on post-oral biopsy instructions. Material and Methods: A questionnaire including two parts was used: 1) Demographic data and self-evaluation of biopsy knowledge by the participants and 2) 11 items about the correct oral biopsy storage and transport to a histopathology laboratory. The data collected from the questionnaires were analyzed by STATA. Results: 48 GDs and 131 DSs participated in this study. The knowledge score of the DSs (5.43±2.01) was significantly lower than GDs (8.33±1.78) (p&lt;0.05). Moreover, there was no significant relationship between GDs' knowledge and their working experience, age, gender and the university they graduated from. However, there was a significant relationship between DSs' school year and their knowledge. Conclusion: The findings showed that the knowledge of DSs was lower than GDs. Since, these students will care for the oral and dental health of the community in the future, upgrading their training (by improving the quantity and quality of theoretical and practical training) is necessary to both understand the different aspects of biopsy, and to be familiar enough with proper oral biopsy storage and transport processes.Objetivo: La biopsia es el estándar de oro para el diagnóstico de lesiones bucales. El manejo y cuidado correctos de la biopsia durante todos los pasos (antes, durante y después de obtener una biopsia) es muy importante para proporcionar muestras de tejido adecuadas para la evaluación microscópica por parte de los patólogos. Este estudio tuvo como objetivo evaluar y comparar los conocimientos de estudiantes de odontología (SD) y dentistas generales (GD) sobre las instrucciones posteriores a la biopsia oral. Material y Métodos: Se utilizó un cuestionario que constaba de dos partes: 1) Datos demográficos y autoevaluación del cono-cimiento de la biopsia por parte de los participantes y 2) 11 ítems sobre el correcto almacenamiento y trans-porte de la biopsia oral a un laboratorio de histopatología. STATA analizó los datos recopilados de los cuestionarios.Resultados: 48 GD y 131 SD participaron en este estudio. La puntuación de conocimiento de los DS (5,43 ± 2,01) fue significativamente menor que la de los GD (8,33 ± 1,78) (p &lt;0,05). Además, no hubo una relación significativa entre los conocimientos de los GD y su experiencia laboral, edad, género y la universidad de la que se graduaron. Sin embargo, hubo una relación significativa entre el año escolar de los DS y sus conocimientos. Conclusión: Los hallazgos mostraron que el conocimiento de los SD era menor que el de los GD. Dado que estos estudiantes se ocuparán de la salud bucodental de la comunidad en el futuro, es necesario mejorar su formación (mejorando la cantidad y calidad de la formación teórica y práctica) tanto para comprender los diferentes aspectos de la biopsia como para estar familiarizados suficientemente con los procesos adecuados de almacenamiento y transporte de biopsias orales