A method for use of machine learning in computer-assisted anatomical prediction. The method includes identifying with a processor parameters in a plurality of training images to generate a training dataset, the training dataset having data linking the parameters to respective training images, training at least one machine learning algorithm based on the parameters in the training dataset and validating the trained machine learning algorithm, identifying with the processor digitized points on a plurality of anatomical landmarks in a radiographic image of a person's skeleton displayed on a screen by determining anatomical relationships of adjacent bony structures as well as dimensions of at least a portion of a body of the skeleton in the displayed image using the validated machine learning algorithm and a scale factor for the displayed image, and making an anatomical prediction of the person's skeletal alignment based on the determined anatomical dimensions and a known morphological relationship.

Disclosed herein are systems, devices, and methods for image-guided surgery. Some systems include a head-mounted unit, having a see-through augmented-reality display and a depth sensor, which is configured to generate depth data with respect to a region of interest (ROI) of a body of a patient that is viewed through the display by a user wearing the head-mounted unit. A processor, which is configured to receive a three-dimensional (3D) tomographic image of the body of the patient, computes a depth map of the ROI based on the depth data generated by the depth sensor, to compute a transformation over the ROI so as to register the tomographic image with the depth map, and to apply the transformation in presenting a part of the tomographic image on the display in registration with the ROI viewed through the display.

The present disclosure relates to the generation of partial surface models from volumetric datasets for subsequent registration of such partial surface models to surface topology datasets. Specifically, given an object that is imaged using surface topology imaging and another volumetric modality, the volumetric dataset is processed in combination with an approach viewpoint to generate one or more partial surfaces of the object that will be visible to the surface topology imaging system. This procedure can eliminate internal structures from the surfaces generated from volumetric datasets, thus increases the similarity of the dataset between the two different modalities, enabling improved and quicker registration.

A method and apparatus for assessing image registration. The method comprises obtaining image datasets for the first and second medical images and registration data representing the registration from the first medical image to the second medical image, collating use-case information for the image registration, deriving a set of at least one measurement and assessment criteria therefor based at least partly on the collated use-case information, performing the at least one measurement on at least one of the obtained image datasets and the obtained registration data to derive at least one measurement value, applying the assessment criteria for the at least one measurement to the at least one measurement value to derive at least one assessment result, and outputting an indication of the at least one assessment result.

Methods and systems for image processing are provided. Image data may be obtained. The image data may include a plurality of voxels corresponding to a first plurality of ribs of an object. A first plurality of seed points may be identified for the first plurality of ribs. The first plurality of identified seed points may be labelled to obtain labelled seed points. A connected domain of a target rib of the first plurality of ribs may be determined based on at least one rib segmentation algorithm. A labelled target rib may be obtained by labelling, based on a hit-or-miss operation, the connected domain of the target rib, wherein the hit-or-miss operation may be performed using the labelled seed points to hit the connected domain of the target rib.

A framework for patient management based on anatomic measurements is described herein. In accordance with one aspect, patient records are clustered into a set of sub-populations based on first anatomic measurements and characteristics extracted from first patient data associated with a population of patients. A representative sub-population similar to a patient may be determined from the set of sub-populations based on the patient data of the patient. A report that presents the second anatomic measurements associated with the patient in relation to corresponding first anatomic measurements associated with the representative sub-population may then be generated.

The present application relates to a method for detecting spinal deformity using three-dimensional ultrasound imaging. A method for detecting spinal deformity using three-dimensional ultrasound imaging, wherein, comprising the following steps: S1. obtaining a three-dimensional image of a spine by a three-dimensional ultrasound imaging system; S2. obtaining axial rotation information of the spine through the three-dimensional image of the spine; S3. using the axial rotation information of the spine to adjust the three-dimensional image of the spine; S4. projecting the adjusted three-dimensional image of the spine after image on a coronal and/or sagittal plane to obtain a projection of the coronal and/or sagittal plane; S5. calculating the spinal deformity data by the projection of the coronal or sagittal plane. This method can more accurately measure the deformity angle of spine in each plane.

A medical image processing method and apparatus, an electronic device, and a storage medium are disclosed. The method includes: detecting a medical image by using a first detection module to obtain first position information of a first target in a second target, wherein the second target comprises at least two of the first targets; segmenting the second target by using the first detection module according to the first position information to obtain a target feature map and first diagnostic auxiliary information of the first target.

Some embodiments of the present disclosure provide a pneumothorax detection method performed by a computing device. The method may comprise obtaining predicted pneumothorax information, predicted tube information, and a predicted spinal baseline with respect to an input image from a trained pneumothorax prediction model; determining at least one pneumothorax representative position for the predicted pneumothorax information and at least one tube representative position for the predicted tube information, in a prediction image in which the predicted pneumothorax information and the predicted tube information are displayed; dividing the prediction image into a first region and a second region by the predicted spinal baseline; and determining a region in which the at least one pneumothorax representative position and the at least one tube representative position exist among the first region and the second region.

The present invention provides a medical image processing method and a computer-readable storage medium. The method includes: reconstructing a two-dimensional cross-sectional image of an imaged tissue based on a volumetric image of the imaged tissue; projecting a CT value of the imaged tissue along a normal direction of the centerline of the imaged tissue in the two-dimensional cross-sectional image; and, positioning the imaged tissue based on the projection result of the CT value of the imaged tissue.