Biomedical Engineering College, Southern Medical University, Guangzhou, People's Republic of China. IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, 130 Mason Farm Road, Chapel Hill, NC 27599-7513, USA.
Phys Med Biol. 2012 Mar 7;57(5):1283-308. doi: 10.1088/0031-9155/57/5/1283. Epub 2012 Feb 17.
Accurate segmentation of the prostate is the key to the success of external beam radiotherapy of prostate cancer. However, accurate segmentation of the prostate in computer tomography (CT) images remains challenging mainly due to three factors: (1) low image contrast between the prostate and its surrounding tissues, (2) unpredictable prostate motion across different treatment days and (3) large variations of intensities and shapes of the bladder and rectum around the prostate. In this paper, an online-learning and patient-specific classification method based on the location-adaptive image context is presented to deal with all these challenging issues and achieve the precise segmentation of the prostate in CT images. Specifically, two sets of location-adaptive classifiers are placed, respectively, along the two coordinate directions of the planning image space of a patient, and further trained with the planning image and also the previous-segmented treatment images of the same patient to jointly perform prostate segmentation for a new treatment image (of the same patient). In particular, each location-adaptive classifier, which itself consists of a set of sequential sub-classifiers, is recursively trained with both the static image appearance features and the iteratively updated image context features (extracted at different scales and orientations) for better identification of each prostate region. The proposed learning-based prostate segmentation method has been extensively evaluated on 161 images of 11 patients, each with more than nine daily treatment three-dimensional CT images. Our method achieves the mean Dice value 0.908 and the mean ± SD of average surface distance value 1.40 ± 0.57 mm. Its performance is also compared with three prostate segmentation methods, indicating the best segmentation accuracy by the proposed method among all methods under comparison.
前列腺的准确分割是前列腺癌外束放射治疗成功的关键。然而，由于以下三个因素，在计算机断层扫描（CT）图像中准确分割前列腺仍然具有挑战性：（1）前列腺与其周围组织之间的图像对比度低，（2）在不同治疗日期间前列腺的运动不可预测，以及（3）前列腺周围的膀胱和直肠的强度和形状变化很大。在本文中，提出了一种基于位置自适应图像上下文的在线学习和患者特定分类方法，以解决所有这些具有挑战性的问题，并实现 CT 图像中前列腺的精确分割。具体来说，沿着患者的规划图像空间的两个坐标方向分别放置两组位置自适应分类器，并使用规划图像以及同一患者的先前分割的治疗图像进行进一步训练，以联合执行新治疗图像（同一患者）的前列腺分割。特别地，每个位置自适应分类器本身由一组顺序子分类器组成，递归地使用静态图像外观特征和迭代更新的图像上下文特征（在不同的尺度和方向上提取）进行训练，以更好地识别每个前列腺区域。基于学习的前列腺分割方法已在 11 名患者的 161 张图像上进行了广泛评估，每个患者都有超过九张每日治疗三维 CT 图像。我们的方法的平均 Dice 值为 0.908，平均平均表面距离值为 1.40±0.57mm。还将其性能与三种前列腺分割方法进行了比较，表明在所比较的所有方法中，该方法具有最佳的分割精度。
