PDF(Pubmed)
Abstract：
In-depth mechanical characterization of veins is required for promising innovations of venous substitutes and for better understanding of venous diseases. Two important physical parameters of veins are shape and thickness, which are quite challenging in soft tissues. Here, we propose the method TREE (TransfeR learning-based approach for thicknEss Estimation) to predict both the segmentation map and thickness value of the veins. This model incorporates one encoder and two decoders which are trained in a special manner to facilitate transfer learning. First, an encoder-decoder pair is trained to predict segmentation maps, then this pre-trained encoder with frozen weights is paired with a second decoder that is specifically trained to predict thickness maps. This leverages the global information gained from the segmentation model to facilitate the precise learning of the thickness model. Additionally, to improve the performance we introduce a sensitive pattern detector (SPD) module which further guides the network by extracting semantic details. The swept-source optical coherence tomography (SS-OCT) is the imaging modality for saphenous varicose vein extracted from the diseased patients. To demonstrate the performance of the model, we calculated the segmentation accuracy-0.993, mean square error in thickness (pixels) estimation-2.409 and both these metrics stand out when compared with the state-of-art methods.
摘要：
静脉替代品的有希望的创新和更好地了解静脉疾病需要静脉的深入机械表征。静脉的两个重要物理参数是形状和厚度，在软组织中相当具有挑战性。这里,我们提出了一种方法TREE（基于TransfeR学习的厚度估计方法）来预测静脉的分割图和厚度值。该模型包含一个编码器和两个解码器，它们以特殊方式训练以促进迁移学习。首先,编码器-解码器对被训练来预测分割图，然后,将这个具有冻结权重的预训练编码器与第二解码器配对,该第二解码器被专门训练以预测厚度图。这利用从分割模型获得的全局信息来促进厚度模型的精确学习。此外,为了提高性能，我们引入了敏感模式检测器（SPD）模块，该模块通过提取语义细节进一步指导网络。扫频源光学相干断层扫描（SS-OCT）是从患病患者中提取的隐静脉曲张的成像方式。为了演示模型的性能，我们计算了分割精度-0.993，厚度（像素）估计的均方误差-2.409，与最先进的方法相比，这两个指标都脱颖而出。
