Progressively-trained scale-invariant and boundary-aware deep neural network for the automatic 3D segmentation of lung lesions
US11232572B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 14, 2020 |
| Grant date | Jan 25, 2022 |
| Priority date | — |
| Expiry date | Jul 16, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30096
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and method are disclosed for segmenting a set of two-dimensional CT slices corresponding to a lesion. In an embodiment, for each of at least a subset of the set of CT slices, the system inputs the CT slice into a plurality of branches of a trained segmentation block. Each branch of the segmentation block includes a convolutional neural network (CNN) with filters at a different scale, and produces one or more levels of output. The system generates, for each CT slice in the subset, feature maps for each level of output. The system generates a segmentation of each CT slice in the subset based on the feature maps of each level of output. The system aggregates the segmentations of each slice in the subset to generate a three-dimensional segmentation of the lesion. The system transmits data representing the three-dimensional segmentation to a user interface for display.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.