【摘要】 胚胎植入前形态学特征是人类体外受精胚胎质量评估的重要依据.目前，胚胎学家主要利用胚胎时差成像（Time-Lapse Imaging,TLI）技术观察胚胎图像形态变化，从而筛选最有发育潜能的胚胎进行移植或冷冻保存.然而，人工评估不仅费时费力，且需要较强的专业知识，并存在一定的主观性等.针对这一问题，该文提出一种基于课程学习的胚胎图像语义分割方法，实现了胚胎细胞、细胞质与雌雄原核的分割，为后续胚胎质量评估提供定量的形态特征参数.首先，利用评估语义分割算法性能的IoU指标（Intersection over Union,IoU）构建课程学习的难度评分函数（Scoring Function,SF），根据SF评分将所有样本从易到难进行排序；再结合SF评分与目标类别数定义课程学习的步调函数（Pacing Functions,PF），构建了难易程度递增的样本子集；最后，设计多阶段渐进式U-net语义分割（Multi-Stage Progressive U-net,MSPU）模型，根据课程难度顺序依次训练不同阶段的网络，从而实现胚胎图像的语义分割.相关实验结果表明，本文提出的MSPU模型在胚胎图像语义分割任务上获得较好的性能，与基准模型相比，IoU值提高1.4%；特别是在较易与较难的分割任务上具有不错的表现，如细胞与雌雄原核的分割IoU值分别提升了4.6%与1.2%.更多还原

【Abstract】 Morphological features of embryo play an important role to evaluate the quality of human embryos in vitro fertilization. Embryologists mainly use time-lapse imaging(TLI) technology to observe the morphological variation of embryo images and select the most potential embryos for subsequent transfer or cryopreservation. However, manual evaluation is not only time-consuming and laborious, but it also demands specialty-oriented skills and has some subjectivities. To solve this problem, we propose a semantic segmentation method of embryo images based on curriculum learning to segment embryo cell, cytoplasm and pronucleus, which can provide the quantitative parameters of morphological feature for the subsequent evaluation of embryo quality. First, IoU(Intersection over Union) index that is usually used to evaluate the performance of semantic segmentation algorithms is adopted to construct a difficulty scoring function(SF) of curriculum learning. All samples are sorted from easy to difficult according to SF value. Second, we define a pace function(PF) by combining the SF and the number of target categories, and sample subsets is established with increasing sequence of difficulty.Lastly, we design a multi-stage progressive U-net(MSPU) model to segment embryo cell, cytoplasm and pronucleus embryo images at pronuclear-stage, in which the network at different stages are trained using the sample subsets with increasing sequence of difficulty. Experimental results demonstrate that our proposed MSPU model obtains a satisfactory performance on the semantic segmentation of embryo images, and IoU is improved by 1.4% compared with Vanilla Baseline.Our proposed MSPU model shows a pleasing consistency on the easy and difficult segmentation tasks, for example the improvement of 4.6% and 1.2% can be obtained for the segmentation of embryonic cells and pronucleus, respectively.更多还原