Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.
Med Phys. 2012 Oct;39(10):6372-87. doi: 10.1118/1.4754304.
The segmentation of prostate in CT images is of essential importance to external beam radiotherapy, which is one of the major treatments for prostate cancer nowadays. During the radiotherapy, the prostate is radiated by high-energy x rays from different directions. In order to maximize the dose to the cancer and minimize the dose to the surrounding healthy tissues (e.g., bladder and rectum), the prostate in the new treatment image needs to be accurately localized. Therefore, the effectiveness and efficiency of external beam radiotherapy highly depend on the accurate localization of the prostate. However, due to the low contrast of the prostate with its surrounding tissues (e.g., bladder), the unpredicted prostate motion, and the large appearance variations across different treatment days, it is challenging to segment the prostate in CT images. In this paper, the authors present a novel classification based segmentation method to address these problems.
To segment the prostate, the proposed method first uses sparse representation based classification (SRC) to enhance the prostate in CT images by pixel-wise classification, in order to overcome the limitation of poor contrast of the prostate images. Then, based on the classification results, previous segmented prostates of the same patient are used as patient-specific atlases to align onto the current treatment image and the majority voting strategy is finally adopted to segment the prostate. In order to address the limitations of the traditional SRC in pixel-wise classification, especially for the purpose of segmentation, the authors extend SRC from the following four aspects: (1) A discriminant subdictionary learning method is proposed to learn a discriminant and compact representation of training samples for each class so that the discriminant power of SRC can be increased and also SRC can be applied to the large-scale pixel-wise classification. (2) The L1 regularized sparse coding is replaced by the elastic net in order to obtain a smooth and clear prostate boundary in the classification result. (3) Residue-based linear regression is incorporated to improve the classification performance and to extend SRC from hard classification to soft classification. (4) Iterative SRC is proposed by using context information to iteratively refine the classification results.
The proposed method has been comprehensively evaluated on a dataset consisting of 330 CT images from 24 patients. The effectiveness of the extended SRC has been validated by comparing it with the traditional SRC based on the proposed four extensions. The experimental results show that our extended SRC can obtain not only more accurate classification results but also smoother and clearer prostate boundary than the traditional SRC. Besides, the comparison with other five state-of-the-art prostate segmentation methods indicates that our method can achieve better performance than other methods under comparison.
The authors have proposed a novel prostate segmentation method based on the sparse representation based classification, which can achieve considerably accurate segmentation results in CT prostate segmentation.
在 CT 图像中对前列腺进行分割对于外束放射治疗至关重要，外束放射治疗是当今前列腺癌的主要治疗方法之一。在放射治疗过程中，高能 X 射线从不同方向辐射前列腺。为了最大限度地提高癌症部位的剂量并最小化周围健康组织（如膀胱和直肠）的剂量，新治疗图像中的前列腺需要精确定位。因此，外束放射治疗的有效性和效率高度依赖于前列腺的精确定位。然而，由于前列腺与其周围组织（如膀胱）对比度低、前列腺不可预测的运动以及不同治疗日之间的外观变化较大，因此在 CT 图像中分割前列腺具有挑战性。在本文中，作者提出了一种新的基于分类的分割方法来解决这些问题。
为了分割前列腺，所提出的方法首先使用基于稀疏表示的分类（SRC）通过像素分类来增强 CT 图像中的前列腺，以克服前列腺图像对比度差的限制。然后，基于分类结果，使用同一位患者的先前分割的前列腺作为患者特定的图谱，将其对齐到当前治疗图像上，最后采用多数投票策略对前列腺进行分割。为了解决传统 SRC 在像素分类中的局限性，特别是为了分割目的，作者从以下四个方面扩展了 SRC：（1）提出了一种判别子字典学习方法，用于为每个类学习训练样本的判别紧凑表示，从而增加 SRC 的判别能力，并将 SRC 应用于大规模像素分类。（2）用弹性网代替 L1 正则化稀疏编码，以在分类结果中获得清晰的前列腺边界。（3）将基于残差的线性回归纳入其中，以提高分类性能并将 SRC 从硬分类扩展到软分类。（4）通过使用上下文信息来迭代 SRC，以迭代地细化分类结果。
该方法已在由 24 名患者的 330 张 CT 图像组成的数据集上进行了全面评估。通过与基于所提出的四个扩展的传统 SRC 进行比较，验证了扩展 SRC 的有效性。实验结果表明，我们的扩展 SRC 不仅可以获得更准确的分类结果，而且还可以获得比传统 SRC 更清晰的前列腺边界。此外，与其他五种最先进的前列腺分割方法的比较表明，与比较的其他方法相比，我们的方法可以实现更好的性能。
作者提出了一种基于基于稀疏表示的分类的新前列腺分割方法，该方法可以在 CT 前列腺分割中实现相当准确的分割结果。
