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首页> 外文期刊>PLoS Computational Biology >Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion
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Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion

机译：通过特征金字塔融合的荧光显微镜图像中形态学复杂真核细胞的检测与分割

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To analyze cell infection and changes in cell morphology in fluorescence microscopy images, proper cell detection and segmentation are required. In automated fluorescence microscopy image analysis, the separation of signals in close proximity is a challenging problem. High cell densities or cluster formations increase the probability of such situations on the cellular level. Another limitation is the detection of morphologically complex cells, such as macrophages or neurons. Their indefinite morphology causes identification issues when looking for slight variations of fixed shapes. Compared to merely segmenting cytoplasm, instance-based segmentation is a much harder task, since the assignment of a cell instance identity to every pixel of the image is required. We present a new deep learning approach for cell detection and segmentation that efficiently utilizes the nucleus channel for improving segmentation and detection performance of cells by incorporating previously learned nucleus features. This is achieved by a fusion of feature pyramids for nucleus detection and segmentation. The performance is evaluated on a microscopic image dataset, containing both nucleus and cell signals.
机译：为了分析细胞感染和荧光显微镜图像中细胞形态的变化，需要适当的细胞检测和分割。在自动荧光显微镜图像分析中，密切接近信号的分离是一个具有挑战性的问题。高细胞密度或簇形成增加了细胞水平的这种情况的可能性。另一个限制是检测形态学上复杂的细胞，例如巨噬细胞或神经元。它们的无限性形态在寻找固定形状的轻微变化时会导致识别问题。与仅分段细胞质相比，基于实例的分割是一个更难的任务，因为需要对图像的每个像素分配小区实例标识。我们提出了一种新的细胞检测和分段的深度学习方法，其通过结合先前学习的核特征，有效地利用细胞核通道来改善细胞的分割和检测性能。这是通过用于核检测和分割的特征金字塔的融合来实现的。在具有核和小区信号的微观图像数据集上评估性能。

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