Invention Grant
- Patent Title: Deep reinforcement learning for recursive segmentation
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Application No.: US16251242Application Date: 2019-01-18
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Publication No.: US10733788B2Publication Date: 2020-08-04
- Inventor: Pascal Ceccaldi , Xiao Chen , Boris Mailhe , Benjamin L. Odry , Mariappan S. Nadar
- Applicant: Siemens Healthcare GmbH
- Applicant Address: DE Erlangen
- Assignee: Siemens Healthcare GmbH
- Current Assignee: Siemens Healthcare GmbH
- Current Assignee Address: DE Erlangen
- Main IPC: G06T15/08
- IPC: G06T15/08 ; G01R33/56 ; A61B6/00 ; G06T7/00 ; G06N20/00 ; G06N3/08

Abstract:
Systems and methods are provided for generating segmented output from input regardless of the resolution of the input. A single trained network is used to provide segmentation for an input regardless of a resolution of the input. The network is recursively trained to learn over large variations in the input data including variations in resolution. During training, the network refines its prediction iteratively in order to produce a fast and accurate segmentation that is robust across resolution differences that are produced by MR protocol variations.
Public/Granted literature
- US20190287292A1 DEEP REINFORCEMENT LEARNING FOR RECURSIVE SEGMENTATION Public/Granted day:2019-09-19
Information query
IPC分类:
G | 物理 |
G06 | 计算;推算或计数 |
G06T | 一般的图像数据处理或产生 |
G06T15/00 | 3D〔三维〕图像的加工 |
G06T15/08 | .体绘制 |