Systems and methods for segmentation of anatomical structures for image-guided surgery
AssigneeIntuitive Surgical Operations, Inc.
InventorsJunning Li, Pechin Chien Pau Lo, Ahmed Taha, Tao Zhao
Life of this patent
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Abstract
A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.
Claims18
A method for image segmentation, the method comprising:
- receiving volumetric image data for an anatomical region;
- generating a first volumetric patch from the volumetric image data;
- generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch using a multi-scale entropy map providing an indication of structural detail between scale levels;
- receiving the second volumetric patch as an input to a convolutional neural network;
- within the convolutional neural network, conducting a down-sampling filter process;
- and
- within the convolutional neural network, conducting an up-sampling filter process.
A non-transitory machine-readable medium comprising a plurality of machine-readable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to perform a method comprising:
- receiving volumetric image data for an anatomical region;
- generating a first volumetric patch from the volumetric image data;
- generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch using a multi-scale entropy map providing an indication of structural detail between scale levels;
- receiving the second volumetric patch as an input to a convolutional neural network;
- within the convolutional neural network, conducting a down-sampling filter process;
- and
- within the convolutional neural network, conducting an up-sampling filter process.
Dependencies are inferred from the claim text.