Abstract: The present disclosure provides an operation method of a prediction system of significant congenital heart disease in infants in infants, which includes steps as follows. The continuous wavelet transformation is performed on the electrocardiogram to obtain the processed electrocardiogram; the processed electrocardiogram is oversampled to obtain multiple electrocardiogram segments; the transfer learning through multiple pre-trained models based on the multiple electrocardiogram segments is used to establish a significant congenital heart disease model.

Abstract: The present disclosure provides an operating method of an identifying and quantizing system, which includes steps as follows. The T2 weighted image is split into a first group of two-dimensional images; the first group of two-dimensional images is inputted into the mask R-CNN model to obtain the first group of two-dimensional parenchymal brain images; a first group of two-dimensional parenchymal brain images is used to form T2 weighted parenchymal brain images; T2 weighted parenchymal brain image is pre-processed to obtain a pre-processed T2 weighted parenchymal brain image; a three-dimensional convolutional neural network model is used to segment and quantize the brain edema area in the pre-processed T2 weighted parenchymal brain image.

Abstract: The present disclosure provides an operation method of a PET (positron emission tomography) quantitative localization system, which includes steps as follows. The PET image and the MRI (magnetic resonance imaging) of the patient are acquired; the nonlinear deformation is performed on the MRI and the T1 template to generate deformation information parameters; the AAL (automated anatomical labeling) atlas is deformed to an individual brain space of the patient, so as to generate an individual brain space AAL atlas, where the AAL atlas and the T1 template are in a same space; lateralization indexes of the ROIs of the individual brain space AAL atlas corresponding to the PET image normalized through the gray-scale intensity are calculated; the lateralization indexes are inputted into one or more machine learning models to analyze the result of determining a target.

Abstract: The present disclosure provides an operating method of a brain imaging neurological abnormality prediction system, which includes steps as follows. The T1-weighted image and the diffusion-weighted image of the patient are acquired; the image process is performed on the T1-weighted image and the diffusion-weighted image to obtain a smoothed brain standard space infarction image; the smoothed brain standard space infarction image is multiplied by and a weighted image for a post-processing to obtain a post-weight image; the post-weight image is inputted to the deep learning cross validation classification model of transfer learning to predict whether the neurological abnormality occurs within a predetermined period after the patient's brain disease.

Abstract: An operation method of a brain amyloid PET processing system includes steps as follows. The whole brain white matter amyloid PET image is extracted from the smoothed amyloid PET image in the range of the whole brain white matter mask of the normalized brain space, and the uptake value with the preset maximum ratio in the whole brain white matter amyloid PET image is calculated; in the smoothed amyloid PET image of the normalized brain space, one or more voxels in the range of the whole brain gray matter mask are marked and counted, in which each voxel uptake value of the one or more voxels is greater than the uptake value of the preset maximum ratio of the whole brain white matter amyloid PET image, and the one or more voxels are used for interpretation training and test of the classification of the machine learning.

Abstract: An operating method of a ketogenic dietary evaluation system includes steps as follows. The electroencephalogram data of a responder group and the electroencephalogram data of a non-responder group are preloaded, in which each electroencephalogram datum includes electroencephalograms of channels. The electroencephalograms of the channels are preprocessed to obtain the preprocessed electroencephalograms of the channels. A connectivity matrix is obtained on a basis of the phase synchronization between each two of the preprocessed electroencephalograms of the channels. The connectivity matrix is sampled and analyzed through different frequency bands and different proportion threshold values to obtain graphical parameters. A predictive model is established on a basis of a reduction rate of a predetermined event of the responder group, a reduction rate of the predetermined event of the non-responder group and the parameters.

Abstract: The present disclosure provides an operating method of a dopamine transporter check system, and the operation method includes steps as follows. A scan image of a subject's brain is obtained from a scan machine, and the scan image is a three-dimensional image. The scan image is aligned to a standard brain space to obtain a standardized scan image. Intensity normalization is performed on the standardized scan image. The standardized scan image after the intensity normalization is converted into a two-dimensional image. A plurality of image data are got from at least one region of interest in the two-dimensional image, and the at least one region of interest includes a left caudate, a left putamen, a right caudate and a right putamen. A dopamine neuron loss degree measurement and evaluation model based on the image data is established through a transfer learning.

Abstract: The present disclosure provides an operating method of a brain imaging neurological abnormality prediction system, which includes steps as follows. The T1-weighted image and the diffusion-weighted image of the patient are acquired; the image process is performed on the T1-weighted image and the diffusion-weighted image to obtain a smoothed brain standard space infarction image; the smoothed brain standard space infarction image is multiplied by and a weighted image for a post-processing to obtain a post-weight image; the post-weight image is inputted to the deep learning cross validation classification model of transfer learning to predict whether the neurological abnormality occurs within a predetermined period after the patient's brain disease.

Abstract: The present disclosure provides an operation method of a PET (positron emission tomography) quantitative localization system, which includes steps as follows. The PET image and the MRI (magnetic resonance imaging) of the patient are acquired; the nonlinear deformation is performed on the MRI and the T1 template to generate deformation information parameters; the AAL (automated anatomical labeling) atlas is deformed to an individual brain space of the patient, so as to generate an individual brain space AAL atlas, where the AAL atlas and the T1 template are in a same space; lateralization indexes of the ROIs of the individual brain space AAL atlas corresponding to the PET image normalized through the gray-scale intensity are calculated; the lateralization indexes are inputted into one or more machine learning models to analyze the result of determining a target.

Abstract: The present disclosure provides an operating method of a dopamine transporter check system, and the operation method includes steps as follows. A scan image of a subject's brain is obtained from a scan machine, and the scan image is a three-dimensional image. The scan image is aligned to a standard brain space to obtain a standardized scan image. Intensity normalization is performed on the standardized scan image. The standardized scan image after the intensity normalization is converted into a two-dimensional image. A plurality of image data are got from at least one region of interest in the two-dimensional image, and the at least one region of interest includes a left caudate, a left putamen, a right caudate and a right putamen. A dopamine neuron loss degree measurement and evaluation model based on the image data is established through a transfer learning.

Abstract: A tumor recurrence prediction device is provided, which includes a data extraction circuit, a memory, and a processor. The data extraction circuit extracts multiple patient clinical data and multiple slice image information; a memory stores multiple instructions; a processor is connected to the data extraction circuit and the memory, and is configured to load and execute the multiple instructions to: receive the multiple patient clinical data and the multiple slice image information; generate clinical feature information and tumor image feature information according to the multiple patient clinical data and the multiple slice image information; train a prediction model according to the clinical feature information and the tumor image feature information; and predict tumor recurrence for patient information of a patient using the prediction model. In addition, a tumor recurrence prediction method is also disclosed here.

Abstract: The present disclosure provides an operating method of a system for analyzing brain tissue based on computerized tomographic imaging, and the operation method includes steps as follows. A computed tomography image of a subject is aligned to a predetermined standard brain space image, to obtain a first normalized test computed tomography image. A voxel contrast of the first normalized test computed tomography image is enhanced to obtain an enhanced first normalized test computed tomography image. The enhanced first normalized test computed tomography image is aligned to an average computed tomographic image of a control group to obtain a second normalized test computed tomography image. An analysis based on the second normalized test computed tomography image and a plurality of computerized tomographic images of the control group is performed to obtain a t-score map.

Abstract: A medical image processing system includes a memory and a processor coupled to each other. The processor accesses and executes instructions which memory stores to perform the following: obtaining a plurality of brain MR images corresponding to a subject, wherein the brain MR images corresponds to a subject brain space; accessing a DBS targets atlas corresponding to a specific stimulation area; transforming the DBS targets atlas from a MNI brain space to the subject brain space based on a DARTEL algorithm; marking at least one coordinate having a largest Voxel value in the brain MR images based on the transformed DBS targets atlas; and storing the brain MR images being targeted with the at least one coordinate into a predetermined format corresponding to a guiding device so that the guiding device displays the brain MR images being targeted with the at least one coordinate for guidance in DBS procedure.

Abstract: A medical image processing system includes a memory and a processor coupled to each other. The processor accesses and executes instructions which memory stores to perform the following: obtaining a plurality of brain MR images corresponding to a subject, wherein the brain MR images corresponds to a subject brain space; accessing a DBS targets atlas corresponding to a specific stimulation area; transforming the DBS targets atlas from a MNI brain space to the subject brain space based on a DARTEL algorithm; marking at least one coordinate having a largest Voxel value in the brain MR images based on the transformed DBS targets atlas; and storing the brain MR images being targeted with the at least one coordinate into a predetermined format corresponding to a guiding device so that the guiding device displays the brain MR images being targeted with the at least one coordinate for guidance in DBS procedure.

Abstract: A method for localizing an intracranial electrode in a subject's brain is provided. The intracranial electrode has at least one electrode contact. The method includes: acquiring a first brain image reconstructed from first image data acquired after electrode-implantation; acquiring a second brain image reconstructed from second image data acquired before the electrode-implantation; co-registering the first brain image and the second brain image to acquire spatial transformation parameters; extracting a first coordinate of the electrode contact from the first brain image; converting the first coordinate into a second coordinate in the second brain image by using the spatial transformation parameters; co-registering the second brain image and a universal brain atlas to define functional zones in the second brain image; and defining a corresponding functional zone where the second coordinate is located. Another alternative method and a system for localizing an intracranial electrode are also provided herein.

Abstract: The present disclosure provides an operating method of an automatic analysis system on magnetic resonance imaging (MRI), which includes steps as follows. Images are received from of the subject's brain from the MRI machine. Contrast-enhanced T1-weighted images and T2-weighted images are obtained from the images, and the pre-processing is performed on the images. The ratio of T2-weighted images to contrast-enhanced T1-weighted images is calculated to generate contrast-enhanced images. The unsupervised clustering is performed on the region of interest in the contrast-enhanced image to separate a cystic part and a non-cystic part so as to calculate the feature parameters. After radiosurgery is performed on the brain tumor corresponding to the region of interest, the volume change of the tumor is analyzed. The linear regression analysis of the feature parameters and the volume change of the tumor is performed for prognostic evaluation.

Abstract: A system for analyzing brain tissue components based on magnetic resonance image. The system includes a memory and a processor. The memory stores instructions. The processor accesses and executes the instructions to perform the following: extracting maps of tissue from a brain magnetic resonance imaging (MRI) corresponding to normal subjects; averaging the maps of tissue according to a number of the normal subjects to generate reference maps that correspond to different tissues; receiving a brain MRI sample having a targeted region; and analyzing the brain MRI sample based on the reference maps and the targeted region to generate an analysis result, in which the analysis result indicates a ratio of tissues in the targeted region of the brain MRI sample.

Abstract: A method for localizing an intracranial electrode in a subject's brain is provided. The intracranial electrode has at least one electrode contact. The method includes: acquiring a first brain image reconstructed from first image data acquired after electrode-implantation; acquiring a second brain image reconstructed from second image data acquired before the electrode-implantation; co-registering the first brain image and the second brain image to acquire spatial transformation parameters; extracting a first coordinate of the electrode contact from the first brain image; converting the first coordinate into a second coordinate in the second brain image by using the spatial transformation parameters; co-registering the second brain image and a universal brain atlas to define functional zones in the second brain image; and defining a corresponding functional zone where the second coordinate is located. Another alternative method and a system for localizing an intracranial electrode are also provided herein.

Abstract: The present disclosure provides an operating method of a system for analyzing brain tissue based on computerized tomographic imaging, and the operation method includes steps as follows. A computed tomography image of a subject is aligned to a predetermined standard brain space image, to obtain a first normalized test computed tomography image. A voxel contrast of the first normalized test computed tomography image is enhanced to obtain an enhanced first normalized test computed tomography image. The enhanced first normalized test computed tomography image is aligned to an average computed tomographic image of a control group to obtain a second normalized test computed tomography image. An analysis based on the second normalized test computed tomography image and a plurality of computerized tomographic images of the control group is performed to obtain a t-score map.

Abstract: A method for localizing an intracranial electrode in a subject's brain is provided. The intracranial electrode has at least one electrode contact. The method includes: acquiring a first brain image reconstructed from first image data acquired after electrode-implantation; acquiring a second brain image reconstructed from second image data acquired before the electrode-implantation; co-registering the first brain image and the second brain image to acquire spatial transformation parameters; extracting a first coordinate of the electrode contact from the first brain image; converting the first coordinate into a second coordinate in the second brain image by using the spatial transformation parameters; co-registering the second brain image and a universal brain atlas to define functional zones in the second brain image; and defining a corresponding functional zone where the second coordinate is located. Another alternative method and a system for localizing an intracranial electrode are also provided herein.