Taipei Veterans General Hospital

National Yang Ming Chiao Tung University

Taipei Medical University

Huai-Che Yang

Cheng-Chia Lee

Syu-Jyun Peng

Wei-Kai Lee

Yu-Te Wu

Chia-Feng Lu

Wen-Yuh Chung

Chih-Chun Wu

Wan-Yuo Guo

Tzu-Hsuan Huang

Chun-Yi Lin

Chih-Ying Huang

Hsiu-Mei Wu

Ya-Xuan Yang

A brain tumor types distinguish system includes an image outputting device and a server computing device. The image outputting device outputs at least three brain images captured from the position of a brain tumor. The server computing device pre-stores a plurality of distinguish pathways corresponding to different types of brain tumors. The server computing device includes an image receiving module, an image pre-processing module, a data comparison module and a distinguish module. The image receiving module receives the brain images. The image pre-processing module pre-processes the brain images to obtain corresponding processed images thereof. The data comparison module compares the brain images and the processed images with the distinguish pathways to obtain at least three comparison results. The distinguish module statistically analyzes the comparison results to obtain a distinguish result.

Brain tumor types distinguish system, server computing device thereof and non-transitory computer readable storage medium

A method for enhancing an accuracy of a benign tumor development trend assessment system includes: a first processing procedure, an image captured before the treatment is inputted to and be processed by a server computing device of the benign tumor development trend assessment system to obtain a first processing result; a second processing procedure, the images captured before and in at least one period after the treatment are inputted to and processed by the server computing device to obtain a second processing result; a trend analyzing procedure, the trend analyzing module of the server computing device analyzes the first processing result, the second processing result and the trend pathways to obtain a tumor development trend result; and a storing procedure, the first processing result, the second processing result and the tumor development trend result are transformed to an individual trend pathway which is stored in the trend analyzing module.

Method for enhancing an accuracy of a benign tumor development trend assessment system

A benign tumor development trend assessment system includes an image outputting device and a server computing device. The image outputting device outputs first/second images captured from the same position in a benign tumor. The server computing device includes an image receiving module, an image pre-processing module, a target extracting module, a feature extracting module and a trend analyzing module. The image receiving module receives the first/second images. The image pre-processing module pre-processes the first/second images to obtain first/second local images. The target extracting module automatically detects and delineates tumor regions from the first/second local images to obtain first/second region of interest (ROI) images. The feature extracting module automatically identifies the first/second ROI images to obtain at least one first/second features. The trend analyzing module analyzes the first/second features to obtain a tumor development trend result.

Benign tumor development trend assessment system, server computing device thereof and computer readable storage medium

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.

Automatic analysis system on magnetic resonance imaging and operation method thereof

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.

System and method for analyzing brain tissue components based on magnetic resonance image

The present disclosure provides a magnetic resonance (MR) image analysis method for a patient who underwent radiotherapy. The method includes the steps: receiving an MR image set of a patient and a dose map of a radiotherapy plan; converting the dose intensity distribution of the dose map into the relative spatial positions in the MR image set; selecting a radiation dose and a radiation exposure region, wherein the radiation exposure region has radiation intensity being equal to or higher than the radiation dose; using the radiation exposure region to determine a region of interest (ROI) in the MR image set; classifying the voxels inside the ROI of the MR image set into different clusters according to the grayscale values of the voxels inside the ROI; and calculating the volume or ratios of the different clusters inside the ROI. The present disclosure also provides a method for evaluating risks of radiotherapy.

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Taipei, Taiwan

Huai-Che Yang