Invention Grant
- Patent Title: 3-D convolutional neural networks for organ segmentation in medical images for radiotherapy planning
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Application No.: US16565384Application Date: 2019-09-09
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Publication No.: US11100647B2Publication Date: 2021-08-24
- Inventor: Stanislav Nikolov , Samuel Blackwell , Jeffrey De Fauw , Bernardino Romera-Paredes , Clemens Ludwig Meyer , Harry Askham , Cian Hughes , Trevor Back , Joseph R. Ledsam , Olaf Ronneberger
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- Main IPC: G06T7/11
- IPC: G06T7/11 ; G06T7/62 ; A61B5/00 ; A61B6/03 ; A61B6/00 ; A61N5/10 ; G06N3/08

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for segmenting a medical image. In one aspect, a method comprises: receiving a medical image that is captured using a medical imaging modality and that depicts a region of tissue in a body; and processing the medical image using a segmentation neural network to generate a segmentation output, wherein the segmentation neural network comprises a sequence of multiple encoder blocks, wherein: each encoder block is a residual neural network block comprising one or more two-dimensional convolutional neural network layers, one or more three-dimensional convolutional neural network layers, or both, and each encoder block is configured to process a respective encoder block input to generate a respective encoder block output wherein a spatial resolution of the encoder block output is lower than a spatial resolution of the encoder block input.
Public/Granted literature
- US20200082534A1 3-D CONVOLUTIONAL NEURAL NETWORKS FOR ORGAN SEGMENTATION IN MEDICAL IMAGES FOR RADIOTHERAPY PLANNING Public/Granted day:2020-03-12
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