Segmenting ultrasound images

US10957045B2 · kind B2 · utility

Assignees

• University of Notre Dame du Lac · US
• Honk Kong Polytechnic University · CN
• The Chinese University of Hong Kong · CN

Inventors

• Danny Chen · Granger, US
• Yizhe Zhang · Austin, US
• Lin Yang · Shandong, CN
• Michael Tin-Cheung Ying · Sha Tin, CN
• Anil Ahuja · Noida, IN

Key dates

• Filing date: Dec 12, 2017
• Grant date: Mar 23, 2021
• Expiry date: Jun 28, 2038

Classification

• Technology area (CPC G): Physics
• CPC primary: G06V2201/03
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Optimizations are provided for segmenting tissue objects included in an ultrasound image. Initially, raw pixel data is received. Here, each pixel corresponds to ultrasound information. This raw pixel data is processed through a first fully convolutional network to generate a first segmentation label map. This first map includes a first set of objects that have been segmented into a coarse segmentation class. Then, this first map is processed through a second fully convolutional network to generate a second segmentation label map. This second map is processed using the raw pixel data as a base reference. Further, this second map includes a second set of objects that have been segmented into a fine segmentation class. Then, a contour optimization algorithm is applied to at least one of the second set of objects in order to refine that object's contour boundary. Subsequently, that object is identified as corresponding to a lymph node.