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摘要翻译：
肉眼观察卵丘卵母细胞复合体只能提供关于其功能能力的有限信息，而分子评估方法繁琐或昂贵。哺乳动物卵母细胞的图像分析可以为解决这一挑战提供有吸引力的替代方案。然而，鉴于需要检查的卵母细胞数量巨大，以及用于鉴定的特征的主观性质，这是复杂的。有监督的机器学习方法，如随机森林，加上专家生物学家的注释，可以使分析任务标准化，减少学科间的可变性。我们提出了一个半自动预测卵母细胞所属类别的框架，该框架基于对获取的显微图像进行多目标参数分割，然后使用随机森林进行基于特征的分类。
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英文标题：
《Grading of Mammalian Cumulus Oocyte Complexes using Machine Learning for
in Vitro Embryo Culture》
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作者：
Viswanath P Sudarshan, Tobias Weiser, Phalgun Chintala, Subhamoy
Mandal, and Rahul Dutta
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最新提交年份：
2016
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Physics 物理学
二级分类：Medical Physics 医学物理学
分类描述：Radiation therapy. Radiation dosimetry. Biomedical imaging modelling. Reconstruction, processing, and analysis. Biomedical system modelling and analysis. Health physics. New imaging or therapy modalities.
放射治疗。辐射剂量学。生物医学成像建模。重建、处理和分析。生物医学系统建模与分析。健康物理学。新的成像或治疗方式。
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英文摘要：
Visual observation of Cumulus Oocyte Complexes provides only limited information about its functional competence, whereas the molecular evaluations methods are cumbersome or costly. Image analysis of mammalian oocytes can provide attractive alternative to address this challenge. However, it is complex, given the huge number of oocytes under inspection and the subjective nature of the features inspected for identification. Supervised machine learning methods like random forest with annotations from expert biologists can make the analysis task standardized and reduces inter-subject variability. We present a semi-automatic framework for predicting the class an oocyte belongs to, based on multi-object parametric segmentation on the acquired microscopic image followed by a feature based classification using random forests.
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PDF链接：
https://arxiv.org/pdf/1603.01739
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