Online updating of context-aware landmark detectors for prostate localization in daily treatment CT images: Online updating of context-aware landmark detectors in daily treatment CT images

In image guided radiation therapy, it is crucial to fast and accurately localize the prostate in the daily treatment images. To this end, the authors propose an online update scheme for landmark-guided prostate segmentation, which can fully exploit valuable patient-specific information contained in the previous treatment images and can achieve improved performance in landmark detection and prostate segmentation

Accurate Segmentation of CT Pelvic Organs via Incremental Cascade Learning and Regression-based Deformable Models

Accurate segmentation of male pelvic organs from computed tomography (CT) images is important in image guided radiotherapy (IGRT) of prostate cancer. The efficacy of radiation treatment highly depends on the segmentation accuracy of planning and treatment CT images. Clinically manual delineation is still generally performed in most hospitals. However, it is time consuming and suffers large inter-operator variability due to the low tissue contrast of CT images. To reduce the manual efforts and improve the consistency of segmentation, it is desirable to develop an automatic method for rapid and accurate segmentation of pelvic organs from planning and treatment CT images. This dissertation marries machine learning and medical image analysis for addressing two fundamental yet challenging segmentation problems in image guided radiotherapy of prostate cancer. Planning-CT Segmentation. Deformable models are popular methods for planning-CT segmentation. However, they are well known to be sensitive to initialization and ineffective in segmenting organs with complex shapes. To address these limitations, this dissertation investigates a novel deformable model named regression-based deformable model (RDM). Instead of locally deforming the shape model, in RDM the deformation at each model point is explicitly estimated from local image appearance and used to guide deformable segmentation. As the estimated deformation can be long-distance and is spatially adaptive to each model point, RDM is insensitive to initialization and more flexible than conventional deformable models. These properties render it very suitable for CT pelvic organ segmentation, where initialization is difficult to get and organs may have complex shapes. Treatment-CT Segmentation. Most existing methods have two limitations when they are applied to treatment-CT segmentation. First, they have a limited accuracy because they overlook the availability of patient-specific data in the IGRT workflow. Second, they are time consuming and may take minutes or even longer for segmentation. To improve both accuracy and efficiency, this dissertation combines incremental learning with anatomical landmark detection for fast localization of the prostate in treatment CT images. Specifically, cascade classifiers are learned from a population to automatically detect several anatomical landmarks in the image. Based on these landmarks, the prostate is quickly localized by aligning and then fusing previous segmented prostate shapes of the same patient. To improve the performance of landmark detection, a novel learning scheme named "incremental learning with selective memory" is proposed to personalize the population-based cascade classifiers to the patient under treatment. Extensive experiments on a large dataset show that the proposed method achieves comparable accuracy to the state of the art methods while substantially reducing runtime from minutes to just 4 seconds.Doctor of Philosoph

Improving radiotherapy using image analysis and machine learning

With ever increasing advancements in imaging, there is an increasing abundance of images being acquired in the clinical environment. However, this increase in information can be a burden as well as a blessing as it may require significant amounts of time to interpret the information contained in these images. Computer assisted evaluation is one way in which better use could be made of these images. This thesis presents the combination of texture analysis of images acquired during the treatment of cancer with machine learning in order to improve radiotherapy. The first application is to the prediction of radiation induced pneumonitis. In 13- 37% of cases, lung cancer patients treated with radiotherapy develop radiation induced lung disease, such as radiation induced pneumonitis. Three dimensional texture analysis, combined with patient-specific clinical parameters, were used to compute unique features. On radiotherapy planning CT data of 57 patients, (14 symptomatic, 43 asymptomatic), a Support Vector Machine (SVM) obtained an area under the receiver operator curve (AUROC) of 0.873 with sensitivity, specificity and accuracy of 92%, 72% and 87% respectively. Furthermore, it was demonstrated that a Decision Tree classifier was capable of a similar level of performance using sub-regions of the lung volume. The second application is related to prostate cancer identification. T2 MRI scans are used in the diagnosis of prostate cancer and in the identification of the primary cancer within the prostate gland. The manual identification of the cancer relies on the assessment of multiple scans and the integration of clinical information by a clinician. This requires considerable experience and time. As MRI becomes more integrated within the radiotherapy work flow and as adaptive radiotherapy (where the treatment plan is modified based on multi-modality image information acquired during or between RT fractions) develops it is timely to develop automatic segmentation techniques for reliably identifying cancerous regions. In this work a number of texture features were coupled with a supervised learning model for the automatic segmentation of the main cancerous focus in the prostate - the focal lesion. A mean AUROC of 0.713 was demonstrated with 10-fold stratified cross validation strategy on an aggregate data set. On a leave one case out basis a mean AUROC of 0.60 was achieved which resulted in a mean DICE coefficient of 0.710. These results showed that is was possible to delineate the focal lesion in the majority (11) of the 14 cases used in the study

Segmentação automática de estruturas pélvicas de imagens de tomografia computadorizada para planejamento da radioterapia de câncer de próstata

Orientador : Prof. Dr. Volmir Eugênio WilhelmTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 05/04/2017Inclui referências : f. 100-111Resumo: Nos últimos anos, o aumento da incidência de casos de câncer de próstata vem se tornando um desafio para a ciência médica. Uma das modalidades de tratamento é a radioterapia de intensidade modulada, IMRT, que permite conformar o feixe de dose de radiação em imagens de duas ou três dimensões. Uma das fases do planejamento consiste na segmentação das imagens das estruturas de interesse, tais como tumores e órgãos nobres e saudáveis, que é realizada manualmente, tarefa que demanda tempo do especialista, limitando o número de planejamentos efetuados. Dessa forma, é proposto um algoritmo de segmentação automática para as estruturas de interesse da região pélvica masculina de imagens de tomografia computadorizada (TC) para o planejamento da radioterapia de câncer de próstata. Neste trabalho foram utilizadas 300 imagens de TC no padrão DICOM, que correspondem a 10 exames de pacientes. Na segmentação das estruturas de interesse, realizou-se um pré-processamento das imagens (filtragem), em seguida, aplicou-se o método de segmentação Region Growing. Para a segmentação automática da região pélvica masculina utilizando o método Region Growing é necessário a escolha dos pixels sementes, que na maioria dos casos, é realizada observando a imagem e selecionando manualmente um ponto na região de interesse que se quer segmentar. O objetivo é que esses pontos sejam escolhidos de forma automática, sem a interferência do observador. Para isso, é proposto um algoritmo de busca de pixels sementes (ABS) para a segmentação automática da região pélvica masculina, isto é, as regiões de cabeça de fêmur direita e esquerda, bexiga e reto, considerados tecidos nobres para a etapa de planejamento da radioterapia. A tese contou com o envolvimento de profissionais do Hospital Erasto Gaertner, que forneceram as imagens segmentadas manualmente dos pacientes em tratamento para que fosse realizada a comparação com a segmentação automática. Verificou-se que o algoritmo de Region Growing com lançamento automático de sementes teve um índice de similaridade médio, considerando todos os exames estudados de 81;46% para a bexiga e de 60;10% para o reto, e tempo computacional médio de 21,16 segundos. Obteve-se bons resultados confirmados pela equipe de física-médica do hospital Erasto Gaertner. Por conseguinte, a utilização de sistemas assistidos por computador torna-se necessário para superar a demora nesta etapa do planejamento da radioterapia, com uma redução significativa do tempo necessário para a segmentação. Palavras-chave: planejamento da radioterapia, segmentação de imagens, region growing, algoritmo ABS.Abstract: In past years, prostate cancer incidence is growing, and become a challenge for medical science. Intensity modulated radiotherapy, IMRT, is one of the treatment modalities that allow a radiation dose to be conformed into two or three dimensions images. One of the planning stages consists in interest structures segmentation, such as tumors, and healthy and noble organs, which is manually performed, a task that requires specialist time, and limits the number of accomplished plannings. This way, an algorithm for automatic segmentation is proposed to identify interest structures in the male pelvic region, by using computed tomography (CT) images for prostate cancer radiotherapy planning. For this job we used 300 CT images in DICOM standard, that correspond to 10 patients exams. In interest structures segmentation, it was performed an image pre-processing (filtering), and then, it was applied a segmentation method known as Region Growing. To use Region Growing method for male pelvic region automatic segmentation, it's required to choose seed pixels, which in most cases, it's performed by observing the image, and manually selecting one point in the interest region segmentation. The goal is for these points to be chosen automatically, without the observer's interference. For this purpose, a pixels selecting algorithm (ABS) is proposed for an automatic segmentation of the male pelvic region, which covers right and left femur head regions, urinary bladder, and rectum organ, all of them considered as noble tissues for this stage of radiotherapy planning. The thesis had involvement with Hospital Erasto Gaertner staff, that provided manual segmented images from patients under treatment, in order to perform comparison against automatic segmentation. It was possible to verify that the algorithm for growing regions with automatic seed lauching had a mean index similarity, considering all studied exams of 81;46% for the bladder, and 60;10% for rectum organ, and the average computational time of 21,16 seconds. Good results were obtained, confirmed by the Medical physics team of Erasto Gaertner Hospital. Therefore, the use computer-aided systems become necessary in order to overcome the delay in this stage of planning for the radiotherapy, with a significant time reduction needed for the segmentation. Keywords: radiotherapy planning, images segmentation, region growing, ABS algorithm

Medical Informatics and Data Analysis

During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role