Nuclei segmentation in histopathology images using deep neural networks
作者： Peter Naylor , Marick Lae , Fabien Reyal , Thomas Walter
DOI: 10.1109/ISBI.2017.7950669
关键词: Artificial neural network 、 Benchmark (computing) 、 Digital pathology 、 Artificial intelligence 、 Set (abstract data type) 、 Data set 、 Computer vision 、 Image segmentation 、 Histopathology 、 Cancer 、 Computer science
摘要: Analysis and interpretation of stained tumor sections is one of the main tools in cancer diagnosis and prognosis, which is mainly carried out manually by pathologists. The avent of digital pathology provides us with the challenging opportunity to automatically analyze large amounts of these complex image data in order to draw biological conclusions from them and to study cellular and tissular phenotypes at a large scale. One of the bottlenecks for such approaches is the automatic segmentation of cell nuclei from this type of image data. Here …
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