69 results about "Bone segmentation" patented technology

A method and apparatus for deep learning based automatic bone removal in medical images, such as computed tomography angiography (CTA) volumes, is disclosed. Bone structures are segmented in a 3D medical image of a patient by classifying voxels of the 3D medical image as bone or non-bone voxels using a deep neural network trained for bone segmentation. A 3D visualization of non-bone structures in the 3D medical image is generated by removing voxels classified as bone voxels from a 3D visualization of the 3D medical image.

A method of automatically identifying bone components in a medical image data set of voxels, the method comprising:a) applying a first set of one or more tests to accept voxels as belonging to seeds, wherein none of the tests examine an extent to which the image density has a local maximum at or near a voxel and falls steeply going away from the local maximum in both directions along an axis;b) applying a second set of one or more tests to accept seeds as bone seeds, at least one of the tests requiring at least one voxel belonging to the seed to have a local maximum in image density at or near said voxel, with the image density falling sufficiently steeply in both directions along at least one axis; andc) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.

The invention discloses an image processing method and system, and image processing model training method and system. The image processing method comprises: obtaining a to-be-detected image; inputtingthe to-be-detected image into a neural network model for processing to obtain a bone segmentation result, a bone center line segmentation result and a bone fracture detection result; wherein the neural network model is determined by machine training learning based on a training image. The bone segmentation, bone center line segmentation and bone fracture detection functions are achieved at the same time through the trained deep learning network, the total consumed time can be shortened by 50%, the memory space of the model can be saved by 40%, and meanwhile a doctor can be helped to reduce the film reading burden, accelerate the film reading time, reduce the missed diagnosis probability and reduce the contradiction between the doctor and the patient.

A method for segmenting bones on magnetic resonance (MR) images includes retrieving an MR image and performing an enhancement process on the MR image to generate a bone enhanced MR image. The bone enhanced MR image is then registered to a computer tomography (CT) based bone atlas. An MR image with bone segmentation is generated by segmenting the bone enhanced MR image using the CT based bone atlas as a mask. The MR image with bone segmentation may be presented on a display.

The invention relates to the technical field of medical treatment, and discloses a navigation method and system for hip and knee joint replacement. The method comprises the following steps: acquiringa CT image; performing leg bone segmentation and reconstruction according to the CT image to obtain a leg bone model in model space; based on a leg bone marker, performing spatial pose registration onthe leg bone model and a real leg bone, and establishing a mapping relationship of the leg bone in the model space and the real space; tracking the leg bone marker; updating the real-time pose of theleg bone model; based on a cutting seam marker, performing spatial pose registration on a osteotomy guide block cutting seam model and the osteotomy guide block cutting seam, and establishing a mapping relationship of the osteotomy guide block cutting seam in the model space and the real space; tracking the cutting seam marker; updating the real-time pose of the osteotomy guide block cutting seammodel; and displaying the real-time poses of the osteotomy guide block cutting seam model and the leg bone model. The method improves the positioning accuracy.

A method and apparatus for deep learning based automatic bone removal in medical images, such as computed tomography angiography (CTA) volumes, is disclosed. Bone structures are segmented in a 3D medical image of a patient by classifying voxels of the 3D medical image as bone or non-bone voxels using a deep neural network trained for bone segmentation. A 3D visualization of non-bone structures in the 3D medical image is generated by removing voxels classified as bone voxels from a 3D visualization of the 3D medical image.

The embodiment of the invention relates to the field of image processing, and discloses a bone segmentation method in a hip joint image, electronic equipment and a storage medium. The method comprises the steps: obtaining a to-be-segmented hip joint image; inputting the to-be-segmented hip joint image into a pre-trained segmentation model, and outputting a segmentation result, wherein a method for obtaining the segmentation model through pre-training comprises the following steps: creating an initial segmentation model, and obtaining a plurality of artificially labeled hip joint sample images to obtain a mask image; inputting the hip joint sample images into a self-attention transformation initial model and a convolutional neural network initial model to respectively obtain a first segmentation result and a second segmentation result; and calculating training loss, and returning the training loss to the initial segmentation model to obtain a final segmentation model. According to the embodiment of the invention, the segmentation result is accurate, the robustness is realized, and bone structures in the hip joint images can be efficiently and automatically segmented, so that clinical doctors are assisted in surgical planning, intraoperative navigation and postoperative evaluation.

A method of automatically identifying bone components in a medical image data set of voxels, the method comprising: a) applying a first set of one or more tests to accept voxels as belonging to seeds, b) applying a second set of one or more tests to accept seeds as bone seeds, and c) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.