MULTI-SCALE 3D CONVOLUTIONAL CLASSIFICATION MODEL FOR CROSS-SECTIONAL VOLUMETRIC IMAGE RECOGNITION
A three dimensional classification system for recognizing cross-sectional images automatically contains a processor that executes: (1) rescaling of a plurality of cross-sectional images; and feeding the rescaled plurality of cross-sectional images into two branches; (2) feeding the rescaled plurality of cross-sectional images into a first branch for performing a plurality of convolutions on the rescaled plurality of cross-sectional images directly to learn features for distinguishing phases; (3) feeding the rescaled plurality of cross-sectional images into a second branch for reducing resolution, and then performing a plurality of convolutions on the reduced resolution plurality of cross-sectional images to learn features for distinguishing phases; and (4) concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly.
1 . A three dimensional classification system for recognizing cross-sectional images automatically, comprising:
a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
rescaling a plurality of cross-sectional images, and feeding the rescaled plurality of cross-sectional images into two branches;
feeding the rescaled plurality of cross-sectional images into a first branch for performing a plurality of convolutions on the rescaled plurality of cross-sectional images directly to learn features for distinguishing phases thereof;
feeding the rescaled plurality of cross-sectional images into a second branch for reducing resolution, then performing a plurality of convolutions on the reduced resolution plurality of cross-sectional images to learn features for distinguishing phases thereof; and
concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly.
2 . The three dimensional classification system of claim 1 , wherein the cross-sectional images comprise computed tomography (CT) images.
3 . The three dimensional classification system of claim 1 , wherein the cross-sectional images comprise magnetic resonance images.
4 . The three dimensional classification system of claim 1 , wherein spatial relationships across slices of the plurality of images are analyzed.
5 . The three dimensional classification system of claim 1 , wherein the first branch performs four convolutions on the rescaled plurality of cross-sectional images.
6 . The three dimensional classification system of claim 1 , wherein the second branch performs four convolutions on the reduced resolution plurality of cross-sectional images.
7 . The three dimensional classification system of claim 1 configured to facilitate an assessment of anatomical structures based upon a stereoscopic volumetric quantification.
8 . A machine learning system comprising the three dimensional classification system of claim 1 .
9 . A method of recognizing cross-sectional images, comprising:
rescaling a plurality of cross-sectional images, and feeding the rescaled plurality of cross-sectional images into two branches;
feeding the rescaled plurality of cross-sectional images into a first branch for performing a plurality of convolutions on the rescaled plurality of cross-sectional images directly to learn features for distinguishing phases;
feeding the rescaled plurality of cross-sectional images into a second branch for reducing resolution, then performing a plurality of convolutions on the reduced resolution plurality of cross-sectional images to learn features for distinguishing phases; and
concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly.
10 . The method of recognizing cross-sectional images of claim 9 , wherein the first branch performs four convolutions on the rescaled plurality of cross-sectional images.
11 . The method of recognizing cross-sectional images of claim 9 , wherein the second branch performs four convolutions on the reduced resolution plurality of cross-sectional images.
12 . The method of recognizing cross-sectional images of claim 9 , further comprising:
facilitating an assessment of anatomical structures based upon a stereoscopic volumetric quantification.
13 . A method of diagnosing cancer comprising using the method of recognizing cross-sectional images of claim 9 .
14 . A non-transitory machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
rescaling a plurality of cross-sectional images, and feeding the rescaled plurality of cross-sectional images into two branches;
feeding the rescaled plurality of cross-sectional images into a first branch for performing a plurality of convolutions on the rescaled plurality of cross-sectional images directly to learn features for distinguishing phases;
feeding the rescaled plurality of cross-sectional images into a second branch for reducing resolution, then performing a plurality of convolutions on the reduced resolution plurality of cross-sectional images to learn features for distinguishing phases; and
concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly.