Department of Medical Physics, Paul Strauss Center, Strasbourg, France.
ICube-UMR 7357, Strasbourg, France.
Comput Biol Med. 2018 Apr 1;95:43-54. doi: 10.1016/j.compbiomed.2018.02.004. Epub 2018 Feb 9.
Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter <1 cm). As part of these treatments, effective detection and precise segmentation of lesions are imperative. Many methods based on deep-learning approaches have been developed for the automatic segmentation of gliomas, but very little for that of brain metastases. We adapted an existing 3D convolutional neural network (DeepMedic) to detect and segment brain metastases on MRI. At first, we sought to adapt the network parameters to brain metastases. We then explored the single or combined use of different MRI modalities, by evaluating network performance in terms of detection and segmentation. We also studied the interest of increasing the database with virtual patients or of using an additional database in which the active parts of the metastases are separated from the necrotic parts. Our results indicated that a deep network approach is promising for the detection and the segmentation of brain metastases on multimodal MRI.
立体定向治疗是目前放射治疗脑转移瘤的参考技术。每次分割的剂量非常高，并且在小体积（直径 <1cm）内进行。作为这些治疗的一部分，有效检测和精确分割病变是至关重要的。已经开发了许多基于深度学习方法的方法来自动分割脑胶质瘤，但很少用于脑转移瘤。我们对现有的 3D 卷积神经网络（DeepMedic）进行了调整，以在 MRI 上检测和分割脑转移瘤。首先，我们试图调整网络参数以适应脑转移瘤。然后，我们通过评估网络在检测和分割方面的性能，探索了单一或联合使用不同 MRI 模式的方法。我们还研究了通过增加虚拟患者数据库或使用将转移瘤的活跃部分与坏死部分分开的附加数据库来增加数据库的兴趣。我们的结果表明，深度网络方法有望在多模态 MRI 上用于检测和分割脑转移瘤。
