Dealing with Topological Information Within a Fully Convolutional Neural Network

被引：3

作者：

Decenciere, Etienne ^{[1
]}

Velasco-Forero, Santiago ^{[1
]}

Min, Fu ^{[2
]}

Chen, Juanjuan ^{[2
]}

Burdin, Helene ^{[3
]}

Gauthier, Gervais ^{[3
]}

Lay, Bruno ^{[3
]}

Bornschloegl, Thomas ^{[4
]}

Baldeweck, Therese ^{[4
]}

机构：

[1] PSL Res Univ, MINES ParisTech, Ctr Math Morphol, Fontainebleau, France

[2] LOreal Res & Innovat, 550 Jinyu Rd, Shanghai, Peoples R China

[3] ADCIS SA, 3 Rue Martin Luther King, F-14280 St Contest, France

[4] LOreal Res & Innovat, 1 Ave Eugene Schueller, F-93601 Aulnay Sous Bois, France

来源：

ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2018 | 2018年 / 11182卷

关键词：

Histological image segmentation; Convolutional neural network; Geodesic operators; Mathematical morphology;

D O I：

10.1007/978-3-030-01449-0_39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A fully convolutional neural network has a receptive field of limited size and therefore cannot exploit global information, such as topological information. A solution is proposed in this paper to solve this problem, based on pre-processing with a geodesic operator. It is applied to the segmentation of histological images of pigmented reconstructed epidermis acquired via Whole Slide Imaging.

引用

页码：462 / 471

页数：10

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