Abstract: Disclosed are a method and an apparatus of processing image. The method includes: obtaining an initial bone segmentation result; and fusing the initial bone segmentation result based on characteristics of and correspondences between a plurality of bone segmentation results in the initial bone segmentation result, to obtain a target bone segmentation result. The initial bone segmentation result includes the plurality of bone segmentation results generated by a plurality of different deep learning models. Methods in the embodiments of the present application can improve precision of a fusion result of the plurality of bone segmentation results.

Abstract: The method for image segmentation includes: acquiring, according to an image to be segmented including a background, a mediastinum, an artery and a vein, a first segmentation result of the mediastinum, the artery, the vein and the background in a mediastinum region of the image to be segmented; acquiring, according to the image to be segmented, a second segmentation result of a blood vessel and the background in an epitaxial region of the image to be segmented; and acquiring, according to the first segmentation result and the second segmentation result, a segmentation result of the mediastinum, the artery, the vein and the background of the image to be segmented, so that the segmentation accuracy and the segmentation efficiency of the artery and the vein may be improved.

Abstract: Disclosed are an image matching method, an image matching device, and a storage medium. A first image sequence and a second image sequence are acquired, and thus a first object and a second object are reconstructed and generated based on the first image sequence and the second image sequence respectively. The registration of the first object and the second object are further performed, and a mapping relationship obtained according to a registration result may indicate a correspondence between image frames in the first image sequence and image frames in the second image sequence. Compared with setting a difference value artificially, obtaining the correspondence between image frames in the first image sequence and image frames in the second image sequence by using the image matching method improves the matching accuracy.

Abstract: Disclosed are a method for image segmentation, and an electronic device. The method includes: performing, based on a preliminary segmentation result of a cerebral hematoma in a head medical image, a secondary segmentation of the cerebral hematoma on a pre-processed head medical image, to obtain a secondary segmentation result of the cerebral hematoma in the pre-processed head medical image; and acquiring a final segmentation result of the cerebral hematoma in the head medical image based on the secondary segmentation result, so that a segmentation effect of a cerebral hematoma in a head medical image can be improved in a case that the head medical image has data heterogeneity.

Abstract: Disclosed are a method, a system, and an apparatus for generating guide information, and an electronic device. The method includes: obtaining image data to be identified of a detection target; segmenting the image data to be identified by using a trained image segmentation model to obtain segmented target image data; determining image data of positioning patches arranged on a body surface of the detection target in the target image data as feature points, and establishing a mapping relationship between a preoperative spatial three-dimensional image coordinate system and an intraoperative spatial positioning coordinate system; and generating guide information based on the mapping relationship, to guide a puncture needle to perform puncturing on the detection target. An operation time of a puncture operation can be greatly shortened, and puncture efficiency and accuracy can be improved.

Abstract: Disclosed are a method and an apparatus of processing image. The method includes: obtaining an initial bone segmentation result; and fusing the initial bone segmentation result based on characteristics of and correspondences between a plurality of bone segmentation results in the initial bone segmentation result, to obtain a target bone segmentation result. The initial bone segmentation result includes the plurality of bone segmentation results generated by a plurality of different deep learning models. Methods in the embodiments of the present application can improve precision of a fusion result of the plurality of bone segmentation results.

Abstract: Disclosed are a segmentation method for a pneumonia sign, a computer-readable storage medium, and an electronic device. A lung region image in a CT image is input into each of a plurality of neural network models to obtain a plurality of pneumonia sign images separately; and the plurality of pneumonia sign images are combined to obtain a pneumonia comprehensive sign image. Pneumonia sign images in a large number of to-be-detected CT images may be obtained efficiently and accurately by using the neural network models, so as to provide reliable data basis for subsequent pneumonia diagnosis.

Abstract: The method for image segmentation includes: acquiring, according to an image to be segmented including a background, a mediastinum, an artery and a vein, a first segmentation result of the mediastinum, the artery, the vein and the background in a mediastinum region of the image to be segmented; acquiring, according to the image to be segmented, a second segmentation result of a blood vessel and the background in an epitaxial region of the image to be segmented; and acquiring, according to the first segmentation result and the second segmentation result, a segmentation result of the mediastinum, the artery, the vein and the background of the image to be segmented, so that the segmentation accuracy and the segmentation efficiency of the artery and the vein may be improved.

Abstract: Disclosed are an image matching method, an image matching device, and a storage medium. A first image sequence and a second image sequence are acquired, and thus a first object and a second object are reconstructed and generated based on the first image sequence and the second image sequence respectively. The registration of the first object and the second object are further performed, and a mapping relationship obtained according to a registration result may indicate a correspondence between image frames in the first image sequence and image frames in the second image sequence. Compared with setting a difference value artificially, obtaining the correspondence between image frames in the first image sequence and image frames in the second image sequence by using the image matching method improves the matching accuracy.

Abstract: A computed tomography (CT) pulmonary nodule detection method based on deep learning is provided. The method comprises the steps of: acquiring 3D pulmonary CT sequence images of a user; processing the acquired 3D pulmonary CT sequence images into 2D image data; inputting 2D image data into a preset deep learning network model for training to obtain a trained pulmonary nodule detection model; inputting a set of 3D pulmonary CT sequence images to be tested into the trained pulmonary nodule detection model to obtain a preliminary pulmonary nodule detection result; applying a pulmonary region segmentation algorithm based on deep learning to the preliminary pulmonary nodule detection result to remove false positive pulmonary nodules, so as to obtain a final pulmonary nodule detection result.

Abstract: A computed tomography (CT) pulmonary nodule detection method based on deep learning is provided. The method comprises the steps of: acquiring 3D pulmonary CT sequence images of a user; processing the acquired 3D pulmonary CT sequence images into 2D image data; inputting 2D image data into a preset deep learning network model for training to obtain a trained pulmonary nodule detection model; inputting a set of 3D pulmonary CT sequence images to be tested into the trained pulmonary nodule detection model to obtain a preliminary pulmonary nodule detection result; applying a pulmonary region segmentation algorithm based on deep learning to the preliminary pulmonary nodule detection result to remove false positive pulmonary nodules, so as to obtain a final pulmonary nodule detection result.

Abstract: An apparatus includes a first circuit and a second circuit. The first circuit may be configured to generate a plurality of delayed signals each as a copy of an input signal shifted in time by a sequence of respective delays based on a control signal. At least two of the respective delays may have a different duration. The first circuit may also be configured to change a number of driver signals that are active during each delay in the sequence of respective delays based on the input signal and the plurality of delayed signals to control a slew rate of an output signal. The second circuit may be configured to drive the output signal in response to the driver signals.

Abstract: An apparatus includes a first circuit and a second circuit. The first circuit may be configured to generate a plurality of delayed signals each as a copy of an input signal shifted in time by a sequence of respective delays based on a control signal. At least two of the respective delays may have a different duration. The first circuit may also be configured to change a number of driver signals that are active during each delay in the sequence of respective delays based on the input signal and the plurality of delayed signals to control a slew rate of an output signal. The second circuit may be configured to drive the output signal in response to the driver signals.

Abstract: An apparatus includes a first circuit and a second circuit. The first circuit may be configured to (i) generate a plurality of delayed signals each as a copy of an input signal shifted in time by a sequence of respective delays based on a control signal and (ii) change a number of driver signals that are active during each delay in the sequence of respective delays based on the input signal and the plurality of delayed signals to control a slew rate of an output signal. The second circuit may be configured to drive the output signal in response to the driver signals.

Abstract: An apparatus includes a first circuit and a second circuit. The first circuit may be configured to (i) generate a plurality of delayed signals each as a copy of an input signal shifted in time by a sequence of respective delays based on a control signal and (ii) change a number of driver signals that are active during each delay in the sequence of respective delays based on the input signal and the plurality of delayed signals to control a slew rate of an output signal. The second circuit may be configured to drive the output signal in response to the driver signals.