PATENT CLAIM ANALYSIS

Application Number: 15893636
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-08
Patent Classification: ["382", "131000"]

Abstract:
There is provided a method for segmentation of an image of a target patient, comprising: providing a target 2D slice and nearest neighbor 2D slice(s) of a 3D anatomical image, and computing, by a trained multi-slice fully convolutional neural network (multi-slice FCN), a segmentation region including a defined intra-body anatomical feature that extends spatially across the target 2D slice and the nearest neighbor 2D slice(s), wherein the target 2D slice and each of the nearest neighbor 2D slice(s) are processed by a corresponding contracting component of sequential contracting components of the multi-slice FCN according to the order of the target 2D slice and the nearest neighbor 2D slice(s) based on the sequence of 2D slices extracted from the 3D anatomical image, wherein outputs of the sequential contracting components are combined and processed by a single expanding component that outputs a segmentation mask for the target 2D slice.

Claim (Index 1):
A computer implemented method for automatic segmentation of a target two dimensional (2D) slice of a three dimensional (3D) anatomical image of a target patient and for computation of an indication of a defined intra-body anatomical feature, comprising:\n providing the target 2D slice of the 3D anatomical image of a target individual captured by a medical imaging modality device, and at least one nearest neighbor 2D slice of the 3D anatomical image sequentially adjacent to the target 2D slice, wherein the at least one nearest neighbor 2D slice and the target 2D slice are obtained from a sequence of 2D slices extracted from the 3D anatomical image; computing a segmentation region including a defined intra-body anatomical feature that extends spatially across the target 2D slice and the at least one nearest neighbor 2D slice, the segmentation region computed for the target 2D slices by a trained multi-slice fully convolutional neural network (multi-slice FCN) that receives the target 2D slice and the at least one nearest neighbor 2D slice as input, wherein the target 2D slice and each of the at least one nearest neighbor 2D slices are processed by a corresponding contracting component of a plurality of sequential contracting components of the multi-slice FCN according to the order of the target 2D slice and the at least one nearest neighbor 2D slice based on the sequence of 2D slices extracted from the 3D anatomical image, wherein outputs of the plurality of sequential contracting components are combined and processed by a single expanding component that outputs a segmentation mask for the target 2D slice; and providing an indication of the segmented region including the predefined intra-body anatomical feature for the target 2D slice for presentation on a display.

Metadata:
- Claim Count in Document: 53.0
- Percentile: 88.0
- Lexical Diversity: 2.44928
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15389860', '13811559', '14349096', '11349793', '15883112']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2964095773245288
- 35 USC 102 Novelty (BERT): 0.4876959812696357
- Combined Prediction Score: 0.3155382177190395
- Mean Citation Score: 216.39584800000003
- Max Citation Score: 222.36084
- Similarity Product: 178.0833588211584

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test