Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA.
Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
Med Phys. 2017 Nov;44(11):5768-5781. doi: 10.1002/mp.12528. Epub 2017 Sep 22.
Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images.
We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images.
The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation.
By combining the population learning and patient-specific learning methods, the proposed method is effective for segmenting the prostate on 3D CT images. The prostate CT segmentation method can be used in various applications including volume measurement and treatment planning of the prostate.
在 CT 图像上对前列腺进行分割在前列腺癌的诊断和治疗中有许多应用。由于 CT 图像上软组织对比度低，因此前列腺分割是一项具有挑战性的任务。本文提出了一种基于学习的三维 CT 图像前列腺分割方法。
我们结合基于人群和基于个体的学习方法来分割 CT 图像上的前列腺。人群数据可以提供有用的信息来指导分割处理。由于个体间的差异，个体特定的信息对于提高个体患者的分割准确性特别有用。在这项研究中，我们结合了一种基于人群的学习方法和一种基于个体的学习方法，以提高 CT 图像上前列腺分割的稳健性。我们基于一组前列腺患者的数据训练一个人群模型。我们还基于个体患者的数据训练一个个体特定的模型，并将用户交互标记的信息纳入分割处理中。我们计算两个模型之间的相似度，以获取适用于人群和个体特定的知识来计算像素属于前列腺组织的可能性。开发了一种新的自适应阈值方法，将可能性图像转换为前列腺的二值图像，从而完成 CT 图像上的腺体分割。
使用 92 例患者的三维 CT 容积对所提出的基于学习的分割算法进行了验证。所有的 CT 图像容积都由两位、有临床经验的放射科医生独立手动分割三次，手动分割结果作为评估的金标准。实验结果表明，与手动分割相比，该分割方法的 Dice 相似系数为 87.18%±2.99%。
通过结合人群学习和个体特定学习方法，本文提出的方法可有效分割三维 CT 图像上的前列腺。前列腺 CT 分割方法可用于各种应用，包括前列腺体积测量和治疗计划。
