Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps

被引：7

作者：

Tong, James ^{[1
,2
]}

Mahapatra, Dwarikanath ^{[3
]}

Bonnington, Paul ^{[2
]}

Drummond, Tom ^{[2
]}

Ge, Zongyuan ^{[1
,2
]}

机构：

[1] Monash Univ, Melbourne, Vic, Australia

[2] Airdoc Res, Melbourne, Vic, Australia

[3] Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates

来源：

DOMAIN ADAPTATION AND REPRESENTATION TRANSFER, AND DISTRIBUTED AND COLLABORATIVE LEARNING, DART 2020, DCL 2020 | 2020年 / 12444卷

关键词：

Fine grained segmentation; Registration; Histopathology; SEGMENTATION; MODEL;

D O I：

10.1007/978-3-030-60548-3_5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Image registration is an important part of many clinical workflows and inclusion of segmentation information of structures of interest improves registration performance. We propose to integrate segmentation information in a registration framework using fine grained feature maps obtained in a self supervised manner. Self supervised feature maps enables use of segmentation information despite the unavailability of manual segmentations. Experimental results show our approach effectively replaces manual segmentation maps and demonstrate the possibility of obtaining state of the art registration performance in real world cases where manual segmentation maps are unavailable.

引用

页码：41 / 51

页数：11