Nouranian Saman, Ramezani Mahdi, Spadinger Ingrid, Morris William J, Salcudean Septimu E, Abolmaesumi Purang
IEEE Trans Med Imaging. 2016 Mar;35(3):921-32. doi: 10.1109/TMI.2015.2502540. Epub 2015 Nov 20.
Low-dose-rate prostate brachytherapy treatment takes place by implantation of small radioactive seeds in and sometimes adjacent to the prostate gland. A patient specific target anatomy for seed placement is usually determined by contouring a set of collected transrectal ultrasound images prior to implantation. Standard-of-care in prostate brachytherapy is to delineate the clinical target anatomy, which closely follows the real prostate boundary. Subsequently, the boundary is dilated with respect to the clinical guidelines to determine a planning target volume. Manual contouring of these two anatomical targets is a tedious task with relatively high observer variability. In this work, we aim to reduce the segmentation variability and planning time by proposing an efficient learning-based multi-label segmentation algorithm. We incorporate a sparse representation approach in our methodology to learn a dictionary of sparse joint elements consisting of images, and clinical and planning target volume segmentation. The generated dictionary inherently captures the relationships among elements, which also incorporates the institutional clinical guidelines. The proposed multi-label segmentation method is evaluated on a dataset of 590 brachytherapy treatment records by 5-fold cross validation. We show clinically acceptable instantaneous segmentation results for both target volumes.
低剂量率前列腺近距离放射治疗是通过将小的放射性种子植入前列腺内部，有时也植入前列腺附近来进行的。种子放置的患者特异性靶区解剖结构通常在植入前通过勾勒一组收集到的经直肠超声图像来确定。前列腺近距离放射治疗的护理标准是描绘临床靶区解剖结构，其紧密跟随真实前列腺边界。随后，根据临床指南对边界进行扩展以确定计划靶体积。手动勾勒这两个解剖靶区是一项繁琐的任务，观察者间的变异性相对较高。在这项工作中，我们旨在通过提出一种高效的基于学习的多标签分割算法来降低分割变异性并缩短计划时间。我们在方法中纳入了一种稀疏表示方法，以学习由图像以及临床和计划靶体积分割组成的稀疏联合元素字典。生成的字典固有地捕捉了元素之间的关系，其中还纳入了机构临床指南。所提出的多标签分割方法通过5折交叉验证在一个包含590例近距离放射治疗记录的数据集上进行评估。我们展示了两个靶体积在临床上均可接受的即时分割结果。
