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 16):
A computer implemented method for training a multi-slice FCN for automatic segmentation 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, for each of a plurality of respective sample individuals, a sample 2D slice of a 3D anatomical image of the respective sample 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 sample 2D slice, wherein the at least one nearest neighbor 2D slice and the sample 2D slice are obtained from a sequence of 2D slices extracted from the 3D anatomical image, wherein the sample 2D slice and the at least one nearest neighbor 2D slice each include a respective manual segmentation region denoting a defined intra-body anatomical feature that extends spatially across the sample 2D slice and the at least one nearest neighbor 2D slice; training a multi-slice FCN according to the sample 2D slice and the at least one nearest neighbor 2D slice each including the respective manual segmentation region received for each of the plurality of sample individuals, wherein the sample 2D slice and each of the at least one nearest neighbor 2D slices for each of the plurality of sample individuals 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 sample 2D slice and the at least one nearest neighbor 2D slice based on the sequence of 2D slices extracted from the 3D anatomical image of the respective sample individual, 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 sample 2D slices; and providing the trained multi-slice FCN for computing an indication of a segmented region including a predefined intra-body anatomical feature for an input of a target 2D slice and at least one nearest neighbor 2D slice of a target individual.

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): 0.2969289937382603
- 35 USC 102 Novelty (BERT): 0.4851043605137604
- Combined Prediction Score: 0.3157465304158103
- Mean Citation Score: 216.39584800000003
- Max Citation Score: 222.36084
- Similarity Product: 183.04688403177727

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