Jalalifar Ali, Soliman Hany, Sahgal Arjun, Sadeghi-Naini Ali
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1063-1066. doi: 10.1109/EMBC44109.2020.9175489.
Radiation therapy is a major treatment option for brain metastasis. For radiation treatment planning and outcome evaluation, magnetic resonance (MR) images are acquired before and at multiple sessions after the treatment. Accurate segmentation of brain tumors on MR images is crucial for treatment planning, response evaluation, and developing data-driven models for outcome prediction. Due to the high volume of imaging data acquired from each patient at multiple follow-up sessions, manual tumor segmentation is resource- and time-consuming in clinic, hence developing an automatic segmentation framework is highly desirable. In this work, we proposed a cascaded 2D-3D Unet framework to segment brain tumors automatically on contrast-enhanced T1- weighted images acquired before and at multiple scan sessions after radiotherapy. 2D Unet is a well-known structure for medical image segmentation. 3D Unet is an extension of 2D Unet with a volumetric input image to provide richer spatial information. The limitation of 3D Unet is that it is memory consuming and cannot process large volumetric images. To address this limitation, a large volumetric input of 3D Unet is often patched to smaller volumes which leads to loss of context. To overcome this problem, we proposed using two cascaded 2D Unets to crop the input volume around the tumor area and reduce the input size of the 3D Unet, obviating the need to patch the input images. The framework was trained using images acquired from 96 patients before radiation therapy and tested using images acquired from 10 patients before and at four follow-up scans after radiotherapy. The segmentation results for the images of independent test set demonstrated that the cascaded framework outperformed the 2D and 3D Unets alone, with an average Dice score of 0.9 versus 0.86 and 0.88 for the baseline, and 0.87 versus 0.83 and 0.84 for the first followup. Similar results were obtained for the other follow-up scans.
放射治疗是脑转移瘤的主要治疗选择。对于放射治疗计划和疗效评估，在治疗前及治疗后的多个阶段采集磁共振（MR）图像。在MR图像上准确分割脑肿瘤对于治疗计划、疗效评估以及开发用于预后预测的数据驱动模型至关重要。由于在多个随访阶段从每位患者获取的成像数据量很大，临床中手动进行肿瘤分割既耗费资源又耗时，因此开发一个自动分割框架非常必要。在这项工作中，我们提出了一种级联的2D - 3D Unet框架，用于在放疗前及放疗后多个扫描阶段采集的对比增强T1加权图像上自动分割脑肿瘤。2D Unet是医学图像分割中一种知名的结构。3D Unet是2D Unet的扩展，它以体积输入图像来提供更丰富的空间信息。3D Unet的局限性在于它消耗内存且无法处理大体积图像。为了解决这一局限性，3D Unet的大体积输入通常被分割成较小的体积，这会导致上下文信息丢失。为了克服这个问题，我们提出使用两个级联的2D Unet来裁剪肿瘤区域周围的输入体积并减小3D Unet的输入大小，从而无需对输入图像进行分割。该框架使用96例患者放疗前采集的图像进行训练，并使用10例患者放疗前及放疗后四次随访扫描采集的图像进行测试。独立测试集图像的分割结果表明，级联框架的表现优于单独的2D和3D Unet，基线时平均Dice分数分别为0.9、0.86和0.88，第一次随访时分别为0.87、0.83和0.84。其他随访扫描也得到了类似的结果。
