Identifying and quantizing system, operation method thereof, and non-transitory computer readable medium

1 . An identifying and quantizing system, comprising:

a storage device configured to store at least one instruction; and

a processor coupled to the storage device, and the processor configured to access and execute the at least one instruction for:

splitting a T2 weighted image into a first group of two-dimensional images;

inputting the first group of two-dimensional images into a mask R-CNN (mask region-based convolutional neural network) model to obtain a first group of two-dimensional parenchymal brain images;

using a first group of two-dimensional parenchymal brain images to form a T2 weighted parenchymal brain images;

performing a pre-process on the T2 weighted parenchymal brain image to obtain a pre-processed T2 weighted parenchymal brain image;

using a three-dimensional convolutional neural network model to segment and quantize a brain edema area in the pre-processed T2 weighted parenchymal brain image;

co-registering a contrast enhanced T1 (T1C) weighted image to the T2 weighted image to obtain a co-registered T1C weighted image;

splitting the co-registered T1C weighted image into a second group of two-dimensional images;

inputting the second group of two-dimensional images into the mask R-CNN model to obtain a second group of two-dimensional parenchymal brain images;

using the second group of two-dimensional parenchymal brain images to form a T1C weighted parenchymal brain image;

performing the pre-process on the T1C weighted parenchymal brain image to obtain a pre-processed T1C weighted parenchymal brain image; and

using another three-dimensional convolutional neural network model to segment and quantize a metastatic brain tumor area in the pre-processed T1C weighted parenchymal brain image.

2 . The identifying and quantizing system of claim 1 , wherein the pre-process comprises an image resampling.

3 . The identifying and quantizing system of claim 1 , wherein the pre-process comprises an image normalization.

4 . The identifying and quantizing system of claim 1 , wherein the processor accesses and executes the at least one instruction for:

pre-training the three-dimensional convolutional neural network model, wherein the three-dimensional convolutional neural network model has two convolution paths, one of the two convolution paths extracts each region in training data so as to perform a feature extraction on the each region, another of the two convolution paths selects each corresponding expanded area based on a center point of the each area and performs an under-sampling on the each corresponding expanded area for the feature extraction.

5 . An operation method of an identifying and quantizing system, and the operation method comprising steps of:

splitting a T2 weighted image into a first group of two-dimensional images;

inputting the first group of two-dimensional images into a mask R-CNN model to obtain a first group of two-dimensional parenchymal brain images;

using a first group of two-dimensional parenchymal brain images to form a T2 weighted parenchymal brain images;

performing a pre-process on the T2 weighted parenchymal brain image to obtain a pre-processed T2 weighted parenchymal brain image;

using a three-dimensional convolutional neural network model to segment and quantize a brain edema area in the pre-processed T2 weighted parenchymal brain image;

co-registering a T1C weighted image to the T2 weighted image to obtain a co-registered T1C weighted image;

splitting the co-registered T1C weighted image into a second group of two-dimensional images;

inputting the second group of two-dimensional images into the mask R-CNN model to obtain a second group of two-dimensional parenchymal brain images;

using the second group of two-dimensional parenchymal brain images to form a T1C weighted parenchymal brain image;

performing the pre-process on the T1C weighted parenchymal brain image to obtain a pre-processed T1C weighted parenchymal brain image; and

using another three-dimensional convolutional neural network model to segment and quantize a metastatic brain tumor area in the pre-processed T1C weighted parenchymal brain image.

6 . The operation method of claim 5 , wherein the step of performing the pre-process on the T2 weighted parenchymal brain image comprises:

performing an image resampling on the T2 weighted parenchymal brain image.

7 . The operation method of claim 5 , wherein the step of performing the pre-process on the T2 weighted parenchymal brain image comprises:

performing an image normalization on the T2 weighted parenchymal brain image.

8 . The operation method of claim 5 , further comprising:

pre-training the three-dimensional convolutional neural network model, wherein the three-dimensional convolutional neural network model has two convolution paths, one of the two convolution paths extracts each region in training data so as to perform a feature extraction on the each region, another of the two convolution paths selects each corresponding expanded area based on a center point of the each area and performs an under-sampling on the each corresponding expanded area for the feature extraction.

9 . A non-transitory computer readable medium to store a plurality of instructions for commanding a computer to execute an operation method, and the operation method comprising steps of:

splitting a T2 weighted image into a first group of two-dimensional images;

inputting the first group of two-dimensional images into a mask R-CNN model to obtain a first group of two-dimensional parenchymal brain images;

using a first group of two-dimensional parenchymal brain images to form a T2 weighted parenchymal brain images;

performing a pre-process on the T2 weighted parenchymal brain image to obtain a pre-processed T2 weighted parenchymal brain image;

using a three-dimensional convolutional neural network model to segment and quantize a brain edema area in the pre-processed T2 weighted parenchymal brain image;

co-registering a T1C weighted image to the T2 weighted image to obtain a co-registered T1C weighted image;

splitting the co-registered T1C weighted image into a second group of two-dimensional images;

inputting the second group of two-dimensional images into the mask R-CNN model to obtain a second group of two-dimensional parenchymal brain images;

using the second group of two-dimensional parenchymal brain images to form a T1C weighted parenchymal brain image;

performing the pre-process on the T1C weighted parenchymal brain image to obtain a pre-processed T1C weighted parenchymal brain image; and

using another three-dimensional convolutional neural network model to segment and quantize a metastatic brain tumor area in the pre-processed T1C weighted parenchymal brain image.

10 . The non-transitory computer readable medium of claim 9 , wherein the step of performing the pre-process on the T2 weighted parenchymal brain image comprises:

performing an image resampling on the T2 weighted parenchymal brain image.

11 . The non-transitory computer readable medium of claim 9 , wherein the step of performing the pre-process on the T2 weighted parenchymal brain image comprises:

performing an image normalization on the T2 weighted parenchymal brain image.

12 . The non-transitory computer readable medium of claim 9 , wherein the operation method further comprises:

pre-training the three-dimensional convolutional neural network model, wherein the three-dimensional convolutional neural network model has two convolution paths, one of the two convolution paths extracts each region in training data so as to perform a feature extraction on the each region, another of the two convolution paths selects each corresponding expanded area based on a center point of the each area and performs an under-sampling on the each corresponding expanded area for the feature extraction.