Semi-Automated Diagnosis of Pulmonary Hypertension Using PUMA, a Pulmonary Mapping and Analysis Tool

Pulmonary Arterial Hypertension (PAH) is a progressive, potentially fatal disease that results in the remodeling of the pulmonary vasculature. Currently the gold standard for diagnosis of pulmonary hypertension is through right heart catheterization, an invasive and costly procedure where pressure measurements are made directly within the affected vessels. Since PAH is associated with the remodeling of the pulmonary arteries, others have proposed quantifying the vessel geometry depicted in computed tomography (CT) images as a non-invasive technique for diagnosis of PAH. The work presented here proposes a similar method of diagnosis by defining and incorporating techniques that are both manual in nature in reference to the segmentation process and automated with the modeling and anatomic measurement quantification steps. Data comprised of both normal and disease cases were gathered and the vessel geometry (specifically the pulmonary trunk, right main pulmonary artery and the left main pulmonary artery) were measured both manually and automatically. A comparison of the automated measurements of the vessel geometry to the manual measurements showed no significant difference between the means of the two groups. A significant difference was found between the cases and the controls leading to the possibility of classifying images based on the vessel geometry. Logistic regression and naïve Bayes models were constructed from the data for discriminating the cases from the controls. Overall, the Naïve Bayes model performed better with a higher sensitivity of 42.9% compared to 19% and a small decrease in specificity of 90.9% from 96.6%, and the model is able to classify correctly more of the patients with disease. Due to the permanent nature of the disease a type I error is acceptable; we prefer to classify patients that do not have the disease as positives than vice versa. We found that the segmenting of additional branches of the pulmonary vasculature could provide additional information for the improvement of the models presented here. In conclusion, we were able to quantify the vessel geometry depicted in CT images as a non-invasive technique for diagnosing PAH and we have shown that the two classes of measurements are not significantly different

Vessel identification in diabetic retinopathy

Diabetic retinopathy is the single largest cause of sight loss and blindness in 18 to 65 year olds. Screening programs for the estimated one to six per- cent of the diabetic population have been demonstrated to be cost and sight saving, howeverthere are insufficient screening resources. Automatic screen-ing systems may help solve this resource short fall. This thesis reports on research into an aspect of automatic grading of diabetic retinopathy; namely the identification of the retinal blood vessels in fundus photographs. It de-velops two vessels segmentation strategies and assess their accuracies. A literature review of retinal vascular segmentation found few results, and indicated a need for further development. The two methods for vessel segmentation were investigated in this thesis are based on mathematical morphology and neural networks. Both methodologies are verified on independently labeled data from two institutions and results are presented that characterisethe trade off betweenthe ability to identify vesseland non-vessels data. These results are based on thirty five images with their retinal vessels labeled. Of these images over half had significant pathology and or image acquisition artifacts. The morphological segmentation used ten images from one dataset for development. The remaining images of this dataset and the entire set of 20 images from the seconddataset were then used to prospectively verify generaliastion. For the neural approach, the imageswere pooled and 26 randomly chosenimageswere usedin training whilst 9 were reserved for prospective validation. Assuming equal importance, or cost, for vessel and non-vessel classifications, the following results were obtained; using mathematical morphology 84% correct classification of vascular and non-vascular pixels was obtained in the first dataset. This increased to 89% correct for the second dataset. Using the pooled data the neural approach achieved 88% correct identification accuracy. The spread of accuracies observed varied. It was highest in the small initial dataset with 16 and 10 percent standard deviation in vascular and non-vascular cases respectively. The lowest variability was observed in the neural classification, with a standard deviation of 5% for both accuracies. The less tangible outcomes of the research raises the issueof the selection and subsequent distribution of the patterns for neural network training. Unfortunately this indication would require further labeling of precisely those cases that were felt to be the most difficult. I.e. the small vessels and border conditions between pathology and the retina. The more concrete, evidence based conclusions,characterise both the neural and the morphological methods over a range of operating points. Many of these operating points are comparable to the few results presented in the literature. The advantage of the author's approach lies in the neural method's consistent as well as accurate vascular classification