Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests

Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a nonlocal external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation

A learning-based CT prostate segmentation method via joint transductive feature selection and regression

In1 recent years, there has been a great interest in prostate segmentation, which is a important and challenging task for CT image guided radiotherapy. In this paper, a learning-based segmentation method via joint transductive feature selection and transductive regression is presented, which incorporates the physician’s simple manual specification (only taking a few seconds), to aid accurate segmentation, especially for the case with large irregular prostate motion. More specifically, for the current treatment image, experienced physician is first allowed to manually assign the labels for a small subset of prostate and non-prostate voxels, especially in the first and last slices of the prostate regions. Then, the proposed method follows the two step: in prostate-likelihood estimation step, two novel algorithms: tLasso and wLapRLS, will be sequentially employed for transductive feature selection and transductive regression, respectively, aiming to generate the prostate-likelihood map. In multi-atlases based label fusion step, the final segmentation result will be obtained according to the corresponding prostate-likelihood map and the previous images of the same patient. The proposed method has been substantially evaluated on a real prostate CT dataset including 24 patients with 330 CT images, and compared with several state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of higher Dice ratio, higher true positive fraction, and lower centroid distances. Also, the results demonstrate that simple manual specification can help improve the segmentation performance, which is clinically feasible in real practice

Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images

Automatic and accurate segmentation of the prostate and rectum in planning CT images is a challenging task due to low image contrast, unpredictable organ (relative) position, and uncertain existence of bowel gas across different patients. Recently, regression forest was adopted for organ deformable segmentation on 2D medical images by training one landmark detector for each point on the shape model. However, it seems impractical for regression forest to guide 3D deformable segmentation as a landmark detector, due to large number of vertices in the 3D shape model as well as the difficulty in building accurate 3D vertex correspondence for each landmark detector. In this paper, we propose a novel boundary detection method by exploiting the power of regression forest for prostate and rectum segmentation. The contributions of this paper are as follows: 1) we introduce regression forest as a local boundary regressor to vote the entire boundary of a target organ, which avoids training a large number of landmark detectors and building an accurate 3D vertex correspondence for each landmark detector; 2) an auto-context model is integrated with regression forest to improve the accuracy of the boundary regression; 3) we further combine a deformable segmentation method with the proposed local boundary regressor for the final organ segmentation by integrating organ shape priors. Our method is evaluated on a planning CT image dataset with 70 images from 70 different patients. The experimental results show that our proposed boundary regression method outperforms the conventional boundary classification method in guiding the deformable model for prostate and rectum segmentations. Compared with other state-of-the-art methods, our method also shows a competitive performance

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