An Optical Machine Vision System for Applications in Cytopathology

This paper discusses a new approach to the processes of object detection, recognition and classification in a digital image focusing on problem in Cytopathology. A unique self learning procedure is presented in order to incorporate expert knowledge. The classification method is based on the application of a set of features which includes fractal parameters such as the Lacunarity and Fourier dimension. Thus, the approach includes the characterisation of an object in terms of its fractal properties and texture characteristics. The principal issues associated with object recognition are presented which include the basic model and segmentation algorithms. The self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory is also presented and a novel technique for the creation and extraction of information from a membership function considered. The methods discussed and the algorithms developed have a range of applications and in this work, we focus the engineering of a system for automating a Papanicolaou screening test

Data fusion techniques for biomedical informatics and clinical decision support

Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. The general framework for these applications includes image processing followed by feature development and classification of the detected region of interest (ROI). Image processing methods such as k-means clustering based on color information, dilation, erosion and centroid locating methods were used for ROI detection. The features extracted include texture, color, nuclei-based and triangle features. Analysis and classification was performed using feature- and decision-level data fusion techniques such as support vector machine, statistical methods such as logistic regression, linear discriminant analysis and voting algorithms --Abstract, page iv

Automatic segmentation of regions of interest in vaginal brachytherapy

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2023-2024. Tutor: Aida Niñerola ; Director: Adrià CasamitjanaThe postoperative endometrial carcinoma treatment often includes radiotherapy (external radiotherapy and/or vaginal brachytherapy) to prevent the reappearance of the tumour. This project aims to improve the efficiency of the vaginal brachytherapy treatment by developing an automatic segmentation algorithm capable of delineating both the clinical target volume and the organs at risk, reducing the time required by experts to exert such task. In this project, we develop an AI-based framework that uses a V-Net architecture at its core. To train and evaluate the model, we use retrospective CT images and corresponding manual delineations from patients treated in Hospital Clinic. The creation of the algorithm was achieved successfully, resulting in a completely functional creator of automatic segmentations. About its performance, the results were found satisfactory in the cases of the vagina, the rectum and the bladder, having acceptable discrepancies in the dosimetry output. On the other hand, the bowel and the sigma models would require further improvements since the segmentations obtained didn’t match the ground truth. Overall, the project represents a step forward in the application of artificial intelligence algorithms to radiotherapy related processes

Entity Extraction and Linking for Digital Pathology

In recent years, huge amounts of biomedical data have been produced. The rich information content of such data could be exploited for several purposes including diagnostics and supporting the medical decision-making process. Nevertheless, most of this information is stored to date using unstructured formats, as occurs, for instance, for free-text narrative clinical reports and clinical notes saved in Electronic Health Records (EHRs). Hence, these documents are human-readable but not machine-readable. Despite some Laboratory Information Systems (LISs) support structured data and synoptic reports, the adoption of structured and machine-readable formats is still limited. This poses hindrances to the full exploitation of computational approaches for data analysis, pattern recognition, and any other secondary use in general. To mitigate this, knowledge extraction methods could be used to automatically extract meaningful information from biomedical textual data provided in natural language. In this thesis, we tackle the former issues by investigating the application of different knowledge extraction techniques for free-text clinical reports coming from the digital pathology domain. Firstly, we manually defined curated ground truths containing all the relevant information extracted from a set of clinical reports. Secondly, we implemented several state-of-the-art techniques for knowledge extraction. Then, we evaluated the performance of such knowledge extraction algorithms against the ground truths. From the analyses conducted, it emerges that the effectiveness of knowledge extraction algorithms depends on the variability of the pathology reports examined and on the kind of entities to extract. Hence, most of the algorithmic approaches considered in our analyses obtain different results that varies significantly in terms of precision and recall.In recent years, huge amounts of biomedical data have been produced. The rich information content of such data could be exploited for several purposes including diagnostics and supporting the medical decision-making process. Nevertheless, most of this information is stored to date using unstructured formats, as occurs, for instance, for free-text narrative clinical reports and clinical notes saved in Electronic Health Records (EHRs). Hence, these documents are human-readable but not machine-readable. Despite some Laboratory Information Systems (LISs) support structured data and synoptic reports, the adoption of structured and machine-readable formats is still limited. This poses hindrances to the full exploitation of computational approaches for data analysis, pattern recognition, and any other secondary use in general. To mitigate this, knowledge extraction methods could be used to automatically extract meaningful information from biomedical textual data provided in natural language. In this thesis, we tackle the former issues by investigating the application of different knowledge extraction techniques for free-text clinical reports coming from the digital pathology domain. Firstly, we manually defined curated ground truths containing all the relevant information extracted from a set of clinical reports. Secondly, we implemented several state-of-the-art techniques for knowledge extraction. Then, we evaluated the performance of such knowledge extraction algorithms against the ground truths. From the analyses conducted, it emerges that the effectiveness of knowledge extraction algorithms depends on the variability of the pathology reports examined and on the kind of entities to extract. Hence, most of the algorithmic approaches considered in our analyses obtain different results that varies significantly in terms of precision and recall

Application of Advanced MRI to Fetal Medicine and Surgery

Robust imaging is essential for comprehensive preoperative evaluation, prognostication, and surgical planning in the field of fetal medicine and surgery. This is a challenging task given the small fetal size and increased fetal and maternal motion which affect MRI spatial resolution. This thesis explores the clinical applicability of post-acquisition processing using MRI advances such as super-resolution reconstruction (SRR) to generate optimal 3D isotropic volumes of anatomical structures by mitigating unpredictable fetal and maternal motion artefact. It paves the way for automated robust and accurate rapid segmentation of the fetal brain. This enables a hierarchical analysis of volume, followed by a local surface-based shape analysis (joint spectral matching) using mathematical markers (curvedness, shape index) that infer gyrification. This allows for more precise, quantitative measurements, and calculation of longitudinal correspondences of cortical brain development. I explore the potential of these MRI advances in three clinical settings: fetal brain development in the context of fetal surgery for spina bifida, airway assessment in fetal tracheolaryngeal obstruction, and the placental-myometrial-bladder interface in placenta accreta spectrum (PAS). For the fetal brain, MRI advances demonstrated an understanding of the impact of intervention on cortical development which may improve fetal candidate selection, neurocognitive prognostication, and parental counselling. This is of critical importance given that spina bifida fetal surgery is now a clinical reality and is routinely being performed globally. For the fetal trachea, SRR can provide improved anatomical information to better select those pregnancies where an EXIT procedure is required to enable the fetal airway to be secured in a timely manner. This would improve maternal and fetal morbidity outcomes associated with haemorrhage and hypoxic brain injury. Similarly, in PAS, SRR may assist surgical planning by providing enhanced anatomical assessment and prediction for adverse peri-operative maternal outcome such as bladder injury, catastrophic obstetric haemorrhage and maternal death