Segmenting CT prostate images using population and patient‐specific statistics for radiotherapy

Abstract

Purpose: In the segmentation of sequential treatment-time CT prostate images acquired in imageguided radiotherapy, accurately capturing the intrapatient variation of the patient under therapy is more important than capturing interpatient variation. However, using the traditional deformablemodel-based segmentation methods, it is difficult to capture intrapatient variation when the number of samples from the same patient is limited. This article presents a new deformable model, designed specifically for segmenting sequential CT images of the prostate, which leverages both population and patient-specific statistics to accurately capture the intrapatient variation of the patient under therapy. Methods: The novelty of the proposed method is twofold: First, a weighted combination of gradient and probability distribution function ͑PDF͒ features is used to build the appearance model to guide model deformation. The strengths of each feature type are emphasized by dynamically adjusting the weight between the profile-based gradient features and the local-region-based PDF features during the optimization process. An additional novel aspect of the gradient-based features is that, to alleviate the effect of feature inconsistency in the regions of gas and bone adjacent to the prostate, the optimal profile length at each landmark is calculated by statistically investigating the intensity profile in the training set. The resulting gradient-PDF combined feature produces more accurate and robust segmentations than general gradient features. Second, an online learning mechanism is used to build shape and appearance statistics for accurately capturing intrapatient variation. Results: The performance of the proposed method was evaluated on 306 images of the 24 patients. Compared to traditional gradient features, the proposed gradient-PDF combination features brought 5.2% increment in the success ratio of segmentation ͑from 94.1% to 99.3%͒. To evaluate the effectiveness of online learning mechanism, the authors carried out a comparison between partial online update strategy and full online update strategy. Using the full online update strategy, the mean DSC was improved from 86.6% to 89.3% with 2.8% gain. On the basis of full online update strategy, the manual modification before online update strategy was introduced and tested, the best performance was obtained; here, the mean DSC and the mean ASD achieved 92.4% and 1.47 mm, respectively. Conclusions: The proposed prostate segmentation method provided accurate and robust segmentation results for CT images even under the situation where the samples of patient under radiotherapy were limited. A conclusion that the proposed method is suitable for clinical application can be drawn.

Improved prostate delineation in prostate HDR brachytherapy with TRUS‐CT deformable registration technology: A pilot study with MRI validation

“…Many CT prostate segmentation technologies have been investigated in recent years, such as the models‐based,23, 24, 25 classification‐based26, 27, 28 and registration‐based29, 30 methods. Most of these segmentation approaches are based on the appearance and texture of the prostate gland on CT images.…”

Learning statistical correlation for fast prostate registration in image‐guided radiotherapy

“…2, the first step of the proposed method is to perform statistical shape-based segmentation on the acquired prostate CT images. Our recently developed statistical shape-based segmentation algorithm 30 can not only segment the prostate from the planning and treatment images but also obtain correspondence between all segmented prostate boundaries. 30 In order to establish the dense correspondences (or deformations), also for the points belonging to the nonboundary regions around the prostate, a correspondence interpolation algorithm, e.g., TPS or others, [14][15][16] is needed.…”

“…30 Our main idea is to learn the statistical (deformation) correlation between the prostate boundary and the nonboundary regions (around the prostate) from both the images acquired from the current patient and the images acquired from other training patients. With the learned statistical deformation correlation, the deformations estimated on the prostate boundaries can be used to rapidly predict the deformations on the nonboundary FIG.…”

Medical Image Segmentation Using Descriptive Image Features

“…Toth et al [5] employed a multi-feature appearance model incorporating the mean, standard deviation, range, skewness, and kurtosis of intensity values in the vicinity of each contour point to drive the edge detection. Feng et al [6] presented an image appearance model consisting of gradient feature and probability distribution function feature. During the optimization process, one of the two features is selected for each vertex to guide contour deformation.…”

“…Chen et al [7] proposed to learn a distribution of the intensity histograms in the local neighborhoods of the delineation contours from the training data. The multifeature [5], the probability distribution function feature [6], and the distribution feature [7] are all region-base features, which are more robust to noise but less accurate than gradientbased features. Therefore, it is desirable to find a robust feature descriptor to describe the object boundaries in medical images.…”