Torrents-Barrena Jordina, Piella Gemma, Gratacos Eduard, Eixarch Elisenda, Ceresa Mario, Gonalez Ballester Miguel A
IEEE Trans Med Imaging. 2020 Oct;39(10):3113-3124. doi: 10.1109/TMI.2020.2987981. Epub 2020 Apr 14.
Fetoscopic laser photocoagulation is the most effective treatment for Twin-to-Twin Transfusion Syndrome, a condition affecting twin pregnancies in which there is a deregulation of blood circulation through the placenta, that can be fatal to both babies. For the purposes of surgical planning, we design the first automatic approach to detect and segment the intrauterine cavity from axial, sagittal and coronal MRI stacks. Our methodology relies on the ability of capsule networks to successfully capture the part-whole interdependency of objects in the scene, particularly for unique class instances (i.e., intrauterine cavity). The presented deep Q-CapsNet reinforcement learning framework is built upon a context-adaptive detection policy to generate a bounding box of the womb. A capsule architecture is subsequently designed to segment (or refine) the whole intrauterine cavity. This network is coupled with a strided nnU-Net feature extractor, which encodes discriminative feature maps to construct strong primary capsules. The method is robustly evaluated with and without the localization stage using 13 performance measures, and directly compared with 15 state-of-the-art deep neural networks trained on 71 singleton and monochorionic twin pregnancies. An average Dice score above 0.91 is achieved for all ablations, revealing the potential of our approach to be used in clinical practice.
胎儿镜激光光凝术是治疗双胎输血综合征最有效的方法，双胎输血综合征是一种影响双胎妊娠的疾病，其特征是胎盘血液循环失调，可能对两个胎儿都致命。为了进行手术规划，我们设计了第一种自动方法，用于从轴向、矢状和冠状MRI堆栈中检测和分割子宫内的腔体。我们的方法依赖于胶囊网络成功捕捉场景中物体部分与整体相互依存关系的能力，特别是对于独特的类实例（即子宫内的腔体）。所提出的深度Q-CapsNet强化学习框架基于上下文自适应检测策略构建，以生成子宫的边界框。随后设计了一种胶囊架构来分割（或细化）整个子宫内的腔体。该网络与一个步长nnU-Net特征提取器相结合，该提取器对判别特征图进行编码，以构建强大的初级胶囊。使用13种性能指标对该方法在有无定位阶段的情况下进行了稳健评估，并直接与在71例单胎和单绒毛膜双胎妊娠上训练的15种最先进的深度神经网络进行了比较。所有消融实验的平均Dice分数均高于0.91，这表明我们的方法在临床实践中具有应用潜力。
