PDF(Pubmed)
Abstract：
Laser interstitial thermal therapy (LITT) is a novel minimally invasive treatment that is used to ablate intracranial structures to treat mesial temporal lobe epilepsy (MTLE). Region of interest (ROI) segmentation before and after LITT would enable automated lesion quantification to objectively assess treatment efficacy. Deep learning techniques, such as convolutional neural networks (CNNs) are state-of-the-art solutions for ROI segmentation, but require large amounts of annotated data during the training. However, collecting large datasets from emerging treatments such as LITT is impractical. In this paper, we propose a progressive brain lesion synthesis framework (PAVAE) to expand both the quantity and diversity of the training dataset. Concretely, our framework consists of two sequential networks: a mask synthesis network and a mask-guided lesion synthesis network. To better employ extrinsic information to provide additional supervision during network training, we design a condition embedding block (CEB) and a mask embedding block (MEB) to encode inherent conditions of masks to the feature space. Finally, a segmentation network is trained using raw and synthetic lesion images to evaluate the effectiveness of the proposed framework. Experimental results show that our method can achieve realistic synthetic results and boost the performance of down-stream segmentation tasks above traditional data augmentation techniques.
摘要：
激光间质热疗法（LITT）是一种新型的微创治疗方法，用于切除颅内结构以治疗内侧颞叶癫痫（MTLE）。在LITT之前和之后的感兴趣区域(ROI)分割将使得能够进行自动化病变量化以客观地评估治疗功效。深度学习技术,如卷积神经网络(CNN)是ROI分割的最新解决方案，但在训练过程中需要大量的注释数据。然而,从LITT等新兴治疗方法中收集大型数据集是不切实际的。在本文中,我们提出了一个渐进性脑损伤合成框架（PAVAE）来扩展训练数据集的数量和多样性。具体而言,我们的框架由两个顺序网络组成:一个面罩合成网络和一个面罩引导的病变合成网络.为了更好地利用外部信息在网络培训期间提供额外的监督，我们设计了一个条件嵌入块（CEB）和一个掩码嵌入块（MEB），以将掩码的固有条件编码到特征空间。最后,使用原始和合成病变图像训练分割网络，以评估所提出的框架的有效性。实验结果表明，我们的方法可以获得逼真的合成结果，并提高了下游分割任务的性能，优于传统的数据增强技术。
