Abstract: The present invention disclose a medical image-based system for predicting lesion classification and a method thereof. The system comprises a feature data extracting module for providing a raw feature data based on a medical image, and a predicting module for outputting a predicted class and a risk index according to the raw feature data. The predicting module comprises a classification unit for generating the predicted class and a prediction score corresponding thereto according to the raw feature data, and a risk evaluation unit for generating the risk index according to the prediction score. The system provides medical personnels a reference score and a risk index to determine progression of a certain disease.

Abstract: The present invention relates to a method for measuring muscle mass, including: a first selection step, wherein a frame selection information is obtained by using a frame to select a fascia region from a provided computed tomography image under the condition that the window width ranges from 300 HU to 500 HU and the window level ranges from 40 HU to 50 HU, wherein the selected range of the fascia region includes a muscle; and a second selection step, wherein a muscle information of the muscle is obtained by calculating a pixel value in the frame-selected fascia region under the condition that the HU value of the CT image ranges from ?29 HU to 150 HU.

Abstract: An image segmentation method includes the following steps: obtaining a target image; inputting the target image into a machine learning model to obtain an image segmentation parameter value corresponding to the target image; executing an image segmentation algorithm on the target image according to the image segmentation parameter value to obtain an image segmentation result, wherein the image segmentation result is segmenting the target image into object regions; and displaying the image segmentation result. In addition, an electronic device and storage medium using the method are also provided.

Abstract: An interactive image marking method is introduced. The interactive image marking method includes the following steps, displaying a target image and at least one marked region in the target image; receiving an interactive signal, where the interactive signal corresponds to a first pixel of the target image; calculating a correlation between the first pixel and pixels of the target image, and determining a correlation range in the target image according to the correlation; editing the marked region according to the correlate range; and displaying the edited marked region. In addition, an electronic device and a recording medium using the method are also introduced.

Abstract: The present invention relates to a method for measuring muscle mass, including: a first selection step, wherein a frame selection information is obtained by using a frame to select a fascia region from a provided computed tomography image under the condition that the window width ranges from 300 HU to 500 HU and the window level ranges from 40 HU to 50 HU, wherein the selected range of the fascia region includes a muscle; and a second selection step, wherein a muscle information of the muscle is obtained by calculating a pixel value in the frame-selected fascia region under the condition that the HU value of the CT image ranges from -29 HU to 150 HU.

Abstract: An image segmentation method includes the following steps: obtaining a target image; inputting the target image into a machine learning model to obtain an image segmentation parameter value corresponding to the target image; executing an image segmentation algorithm on the target image according to the image segmentation parameter value to obtain an image segmentation result, wherein the image segmentation result is segmenting the target image into object regions; and displaying the image segmentation result. In addition, an electronic device and storage medium using the method are also provided.

Abstract: An interactive image marking method is introduced. The interactive image marking method includes the following steps, displaying a target image and at least one marked region in the target image; receiving an interactive signal, where the interactive signal corresponds to a first pixel of the target image; calculating a correlation between the first pixel and pixels of the target image, and determining a correlation range in the target image according to the correlation; editing the marked region according to the correlate range; and displaying the edited marked region. In addition, an electronic device and a recording medium using the method are also introduced.