Method and system for anatomical object detection using marginal space deep neural networks

US9668699B2 · kind B2 · utility

Assignee

• Siemens Healthcare Limited · DE

Inventors

• Bogdan Georgescu · Plainsboro, US
• Yefeng Zheng
• Hien Nguyen · Ngọc Lặc, VN
• Vivek Singh · Monmouth Junction, US
• Dorin Comaniciu · Princeton Junction, US
• David Liu · San Francisco, US

Key dates

• Filing date: May 12, 2015
• Grant date: Jun 6, 2017
• Expiry date: May 12, 2035

Classification

• Technology area (CPC G): Physics
• CPC primary: G06V2201/031
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.