Abstract: A medical image diagnostic apparatus according to an embodiment includes processing circuitry and a display. The processing circuitry acquires medical images. The processing circuitry sends the medical images to a medical image processing apparatus that performs disease analysis based on the medical images. The processing circuitry receives the analysis result from the medical image processing apparatus. The display displays warning information based on the analysis result.

Abstract: A radiation image processing device includes: a first estimation section that estimates components of radiation Ra having passed through a subject Obj using a first radiation image taken from the subject Obj; a second estimation section that estimates components of the radiation Ra, which have passed through an additional scattering element EL, using an estimation result of the first estimation section and scattering characteristics f2(X) of the additional scattering element EL; and a first image generation section that generates a second radiation image, which has been transmitted through the subject Obj and the additional scattering element EL, using an estimation result of the second estimation section.

Abstract: Systems and methods for designing and implementing patient-specific surgical procedures and/or medical devices are disclosed. In some embodiments, a method includes receiving intra-operative data during a surgical procedure to install a patient-specific implant in a patient. The system can compare the intra-operative data to a pre-operative plan to determine whether the implant is positioned and located according to the pre-operative plan.

Abstract: Techniques for adjusting radiotherapy treatment for a patient in real time are provided. The techniques include operations comprising: obtaining, during delivery of a radiotherapy treatment fraction to a patient, one or more images of the patient at a first rate; generating patient motion information at a second rate based on the one or more images obtained at the first rate; receiving, during delivery of the radiotherapy treatment fraction, radiotherapy treatment device settings at a third rate; computing, during delivery of the radiotherapy treatment fraction, dose delivered to the patient with a first level of accuracy based on the generated patient motion information and the radiotherapy treatment device settings; and determining, during delivery of the radiotherapy treatment fraction, a real-time measure of accumulated dose delivered to the patient with a second greater level of accuracy than the first level of accuracy using one or more prior dose computations.

Abstract: A method includes receiving a three-dimensional image dataset of a surgical site of a patient. The method also includes segmenting one or more anatomical features of the surgical site based on the three-dimensional image dataset. The method also includes receiving a two-dimensional image of the surgical site of the patient and registering the two-dimensional image to an image from the three-dimensional image dataset. The method also includes displaying a two-dimensional representation of the segmented one or more anatomical features based on the registered two-dimensional image and the image from the three-dimensional image dataset.

Abstract: For creation of a movement-compensated image reconstruction in an x-ray based imaging method an optimal object movement trajectory is determined by application of a trained algorithm to an optimal latent vector and the movement-compensated image reconstruction is created depending on projection images, with the assumption that the object would have moved in accordance with the optimal object movement trajectory while the imaging method was being carried out.

Abstract: A method for computing dimensions of an object in a scene includes: controlling, by a processor, a depth camera system to capture at least a frame of the scene, the frame including a color image and a depth image arranged in a plurality of pixels; detecting, by the processor, an object in the frame; determining, by the processor, a ground plane in the frame, the object resting on the ground plane; computing, by the processor, a rectangular outline bounding a projection of a plurality of pixels of the object onto the ground plane; computing, by the processor, a height of the object above the ground plane; and outputting, by the processor, computed dimensions of the object in accordance with a length and a width of the rectangular outline and the height.

Abstract: An optical coherence tomography (OCT) unit provides a mechanism/method for automatic artery and vein identification. The method may be embodied by specialized algorithm or by a machine learning model, such as a support vector machine or neural network. The present method may identify a vascular configuration that forms a vortex structure in the deep capillary plexus (DCP) of an OCT-based (e.g., OCTA) image of a retina. The identified vortex may then be designated a vein, and other vascular structures from other plexuses, such as from the superficial vascular plexus (SVP), that connect to the identified vortex structure are likewise designated veins. Additional criteria, some based on the vein designations, may be used to identify arteries.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: A self-shielded and computer controlled system for performing non-invasive stereotactic radiosurgery and precision radiotherapy using a linear accelerator mounted within a two degree-of-freedom radiation shield coupled to a three-degree of freedom patient table is provided. The radiation shield can include an axial shield rotatable about an axial axis and an oblique shield independently rotatable about an oblique axis, thereby providing improved range of trajectories of the therapeutic and diagnostic radiation beams. Such shields can be balanced about their respective axes of rotation and about a common support structure to facilitate ease of movement. Such systems can further include an imaging system to accurately deliver radiation to the treatment target and automatically make corrections needed to maintain the anatomical target at the system isocenter.

Abstract: Methods and systems for training a machine learning model include generating pairs of training pixel patches from a dataset of training images, each pair including a first patch representing a part of a respective training image, and a second patch, centered at the same location as the first, representing a larger part of the training image, being resized to a same size of as the first patch. A detection model is trained using the first pixel patches, to detect and locate cells in the images. A classification model is trained using the first pixel patches, to classify cells according to whether the detected cells are cancerous, based on cell location information generated by the detection model. A segmentation model is trained using the second pixel patches, to locate and classify cancerous arrangements of cells in the images.

Abstract: A convolutional neural network (CNN) is employed in an automated feature and/or anomaly detection system for analyzing images provided by X-ray imaging system. The automated detection system operates in a manner that reduces the number of full-resolution CNN convolution layers required in order to speed up the network inference and learning processes for the detection system. To do so, the detection system utilizes as an input a more compact representation of the tomographic data to alleviate the CNN memory footprint and computation time issues in prior art X-ray systems.

Abstract: A device of an embodiment includes: an optical mechanism; a control computation unit that controls driving of the optical mechanism, acquires a tomographic image of the skin by processing an optical interference signal from the optical system, and calculates a network of blood vessels on the basis of the tomographic image; and a display device that displays the network of blood vessels. The control computation unit expresses thicknesses of the network of blood vessels by defining, as a blood vessel radius, a radius from a set of blood vessel corresponding coordinates determined to be a blood vessel in the calculation of the network of blood vessels and within which other sets of blood vessel corresponding coordinates are present and, superimposing distinctions based on magnitudes of blood vessel radii from the respective sets of blood vessel corresponding coordinates onto the image of the network of blood vessels.

Abstract: The present disclosure relates to image learning method, apparatus, program, and recording medium using a generative adversarial network. The present disclosure allows to learn various images as well as medical radiographic images to maintain structural information on the basis of a generative adversarial network. The present disclosure prevents the structural information of the generated image with respect to an original image from being lost, and improves image qualities, such as resolution, noise degree, contrast, etc. to the level of a target reference dataset. When the present disclosure is used for image standardization, medical radiographic images imaged by different institutions and any number of image datasets having various qualities can be standardized universally.

Abstract: Systems and methods for a pre-treatment quality assurance (QA) of a radiotherapy device may be provided. The method may include determining a measured dose image through an electronic portal dose imaging device (EPID). The method may include determining an energy fluence distribution map related to radiation beams predicted by a first portal dose prediction model. The method may include determining a predicted dose image based on the energy fluence distribution map and a simulated energy response curve related to the EPID. The method may further include determining differences between the measured and predicted dose images by comparing the dose distributions of the measured and predicted dose images.

Abstract: Digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. Uncertainty is resolved according to expected characteristics. The label of any image element with an uncertainty above a threshold is replaced with a new label corresponding to a segmentation class based on domain knowledge.

Abstract: Disclosed is a method and apparatus for quantifying severity of infectious disease based on a vision transformer using a chest X-ray (CXR) image. Here, a method of quantifying severity of infectious disease based on a vision transformer includes receiving an input CXR image; extracting a feature map from the received input CXR image using a pretrained neural network; classifying a lesion in the input CXR image using the vision transformer based on the extracted feature map; and quantifying severity of the input CXR image based on the extracted feature map and the classified lesion.

Abstract: A vessel image, which maps a vessel structure, and a device image, which maps a device, are created. A processor, depending on the device image and the vessel image, creates an overlaying image. At least one filter algorithm is applied to the device image, creating a filtered device image. The overlaying image is created by overlaying of the vessel image with the filtered device image.

Abstract: A method is provided for enhancing video images in a medical device. The method includes receiving a first image frame and a second image frame from one or more image sensors. The first image sub-blocks are generated by dividing the first image frame. At least one curve to the first image sub-blocks are associated based on one or more look-up tables. A target in at least one of the first image sub-blocks is identified. Second image sub-blocks are generated by dividing the second image frame. At least one curve is associated to the second image sub-blocks based on the one or more look-up tables. The target is identified in at least one of the second image sub-blocks. Histogram enhanced images of the target in the first image sub-blocks and the second image sub-blocks are generated. A video image stream is generated based on the histogram enhanced images of the target.

Abstract: The medical image processing apparatus includes: an image acquiring unit that acquires a medical image; a classification unit that classifies, on the basis of the medical image acquired by the image acquiring unit, the medical image or a region of interest included in the medical image; a notification information generating unit that generates, in accordance with a classification result of the classification, first notification information for display and second notification information for storage, the second notification information differing from the first notification information; and a storage that stores the medical image and the second notification information.

Abstract: We present a new method to automatically sample the tumour/stroma interface zone (IZ) from microscopy image analysis data. It first delineates the tumour edge using a set of explicit rules in grid-subsampled tissue areas; then the IZ of controlled width is sampled and ranked by the distance from the edge to compute TIL density profiles across the IZ. From this data, a set of novel Immunogradient indicators are computed to reflect TIL “gravitation” towards the tumour. We applied the method on CD8 immunohistochemistry images of surgically excised breast and colorectal cancers to predict overall patient survival. In both patient cohorts, we found strong and independent prognostic value of the Immunogradient indicators, outperforming methods currently available. We conclude that data-driven, automated, human operator-independent IZ sampling enables precise spatial immune response measurement in the tumour/host interaction frontline for prediction of disease and therapy outcomes.

Abstract: This disclosure discloses a method and system for predicting disease quantification parameters for an anatomical structure. The method includes extracting a centerline structure based on a medical image. The method further includes predicting the disease quantification parameter for each sampling point on the extracted centerline structure by using a GNN, with each node corresponds to a sampling point on the extracted centerline structure and each edge corresponds to a spatial constraint relationship between the sampling points. For each node, a local feature is extracted based on the image patch for the corresponding sampling point by using a local feature encoder, and a global feature is extracted by using a global feature encoder based on a set of image patches for a set of sampling points, which include the corresponding sampling point and have a spatial constraint relationship defined by the centerline structure.

Abstract: An image processing apparatus for determining a radiation field of radiation from a radiation image comprises: a prediction unit that predicts, based on prior information, a degree that each position of the radiation image becomes a predetermined portion of the radiation field and generating a prediction result representing a distribution of the predicted degrees; an analysis unit that analyzes the radiation image to determines the degree that each position of the radiation image becomes the predetermined portion of the radiation field and generating an analysis result representing the distribution of the predicted degrees; and a recognition unit that decides the predetermined portion of the radiation field based on a combining result obtained by combining the prediction result and the analysis result.

Abstract: A method for analysing images in a computer aided diagnosis system (CADx) to provide a first image analysis score and a second image analysis score for an image is described. The method comprising; receiving an input comprising at least one input image showing all or part of the lungs of a subject; analysing the input to calculating a first image analysis value and a second image analysis value for the input and processing the calculated values to generate corresponding first image analysis and second image analysis scores and outputting at least one of the first image analysis score and the second image analysis score for the subject. A computer aided diagnosis system (CADx) and a method of training a computer aided diagnosis system are also described.

Abstract: An apparatus for stent visualization includes a hardware processor that is configured to input one or more stent images from a sequence of X-ray images and corresponding balloon marker location data to a cascaded spatial transform network. The background is separated from the one or more stent images using the cascaded spatial transform network and a transformed stent image with a clear background and a non-stent background image is generated. The stent layer and non-stent layer are generated using a neural network without online optimization. A mapping function f maps the inputs, the sequence images and marker coordinates, into the two single image outputs.

Abstract: A method for generating a panoramic layer image of an object to be imaged, by employing a 2D panoramic X-ray device, the object being imaged by projecting X-rays generated by an X-ray source through the object in a projection direction and recording the X-rays with an X-ray detector. Several 2D X-ray projection images are continuously recorded from various imaging directions while the X-ray source and the X-ray detector move around the object. At least one panoramic layer image is calculated from the recorded 2D X-ray projection images using a reconstruction method. The panoramic layer image is calculated by selecting and employing at least one partial area from at least two 2D X-ray projection images in relation to an active sensor area.

Abstract: Systems and methods are provided for imaging coronary trees via registration of angiographic projections with computed tomographic data. In one example, a method for imaging a coronary artery of interest in a patient may include acquiring computed tomography (CT) imaging data depicting a coronary tree, acquiring a single angiographic projection of the coronary artery of interest, registering the single angiographic projection to the CT imaging data, and determining a fractional flow reserve (FFR) of the coronary artery of interest based on the single angiographic projection registered to the CT imaging data.

Abstract: Various methods and systems are provided for x-ray imaging. In one embodiment, a method includes acquiring, with an x-ray detector, an x-ray image of a subject, determining a transformation that minimizes anti-scatter grid artifacts in the x-ray image, correcting the x-ray image according to the transformation to generate a corrected image, and outputting the corrected image. In this way, artifacts arising from a misalignment of an anti-scatter grid between the calibration and the acquisition may be reduced.

Abstract: The present disclosure relates to a method and apparatus for automatic head and neck organ segmentation.

Abstract: A diagnosis assisting system, a diagnosis assisting method, and a diagnosis assisting program provide information that contributes to an objective diagnosis, with a server of a diagnosis assisting system including a learned model acquisition unit that acquires a plurality of learned models, each of which is generated by machine learning, inputs information based on an image obtained by imaging a diagnostic target, and outputs information that indicates a diagnosis result of the diagnostic target; an image acquisition unit that acquires an image to be analyzed; a calculation unit that performs calculation based on the plurality of learned models acquired on the image acquired, and calculates one piece of information about a diagnosis using the plurality of learned models; and an output unit that outputs the information calculated by the calculation unit.

Abstract: According to one embodiment, a medical information processing system includes processing circuitry. The processing circuitry acquires an ultrasound image of a subject. The processing circuitry acquires a first modality image of the subject, the first modality image differing from the ultrasound image. The processing circuitry acquires an imaging position for the ultrasound image. The processing circuitry generates diagnosis support information for the subject based on the ultrasound image, the imaging position for the ultrasound image, and the first modality image.

Abstract: The present invention makes it possible to estimate, with high precision, a candidate region indicating each of multiple target objects included in an image. A parameter determination unit 11 determines parameters to be used when detecting a boundary line of an image 101 based on a ratio between a density of boundary lines included in an image 101 and a density of boundary lines in a region indicated by region information 102 indicating the region including at least one of the multiple target objects included in the image 101. A boundary line detection unit 12 detects the boundary line in the image 101 using the parameter. For each of the multiple target objects included in the image 101, the region estimation unit 13 estimates the candidate region of the target object based on the detected boundary line.

Abstract: Various examples are directed to apparatus and methods for enhancing an X-ray medical image. In one example, a method for X-ray dental images enhancement is provided. The method includes receiving an input image and applying the noise reduction trough Recursive Locally-Adaptive Wiener Filtration (RLA-WF) based on the sliding window, which is calculated following a recursive two-dimensional scheme; contrast enhancement to the filtered image, including using recursive Sliding Window Contrast Limited Adaptive Histogram Equalization (SW-CLAHE) utilizing a Truncation Threshold Surface (TTS) for calculation of the truncation threshold for the local histogram in each sliding window. The method also includes applying sharpness enhancement to the input image, including using Fast Recursive Adaptive Unsharp Masking (FRA-UM) utilizing a sliding window with two or one-dimensional recursion, to obtain a sharpened image.

Abstract: One or more example embodiments of the present invention relates to a method for the automated determination of examination results in an image sequence from multiple chronologically consecutive frames, the method comprising determining diagnostic candidates in the form of contiguous image regions in the individual frames for a predefined diagnostic finding; and for a number of the diagnostic candidates, determining which candidate image regions in other frames correspond to the particular diagnostic candidate, determining whether the candidate image regions of the particular diagnostic candidate in the other frames overlap with other diagnostic candidates, generating a graph containing the determined diagnostic candidates of the frames as nodes and the determined overlaps as edges, and generating communities from nodes connected via edges.

Abstract: A medical image region screening method and apparatus and a storage medium are provided. The method includes: obtaining a medical image of biological tissue, segmenting tissue regions of a plurality of tissue types from the medical image, selecting, from the tissue regions of the plurality of tissue types based on types of capturing positions of the medical image, a reserved region, obtaining a positional relationship between the reserved region and a predicted lesion region in the medical image; and screening for the predicted lesion region in the medical image based on the positional relationship, to obtain a target lesion region.

Abstract: Methods, devices, and systems that are related to facilitating an automated, fast and accurate model for cardiac image segmentation, particularly for image data of children with complex congenital heart disease are disclosed. In one example aspect, a generative adversarial network is disclosed. The generative adversarial network includes a generator configured to generate synthetic imaging samples associated with a cardiovascular system, and a discriminator configured to receive the synthetic imaging samples from the generator and determine probabilities indicating likelihood of the synthetic imaging samples corresponding to real cardiovascular imaging sample. The discriminator is further configured to provide the probabilities determined by the discriminator to the generator and the discriminator to allow the parameters of the generator and the parameters of the discriminator to be adjusted iteratively until an equilibrium between the generator and the discriminator is established.

Abstract: Technology described herein can verify readiness of a customer tenant/customer for cloud provisioning based on the customer tenant. An example method comprises collecting, by a system comprising a processor, data based on user input from a customer tenant via a user interface, the data comprising information of a configuration setting that is to be used for cloud provisioning of a device based on the configuration setting. The method comprises, in response to collecting the configuration setting, analyzing, by the system, the configuration setting by comparing the configuration setting with respect to a selected rules check. The method comprises, in response to the analyzing of the configuration setting, determining, by the system, whether the configuration setting is acceptable for use in the cloud provisioning based on the configuration setting.

Abstract: Provided is a method for acquiring labeled data, including acquiring a 3D-3D registration sample, wherein the 3D-3D registration sample includes a sample 3D image, a reference 3D image and 3D-3D registration data, and the 3D-3D registration data includes correspondence data between the sample 3D image and the reference 3D image; generating at least one dual-2D-image based on the sample 3D image, wherein each dual-2D-image is a combination of 2D projection images of the sample 3D image in two different directions; and generating 2D-3D registration data corresponding to each dual-2D-image based on the 3D-3D registration data, wherein each 2D-3D registration data includes correspondence data between the dual-2D-image and the reference 3D image.

Abstract: A neural network system implements a model for generating an output image based on a received input image. The model is learned through a training process during which parameters associated with the model are adjusted so as to maximize a difference between a first image predicted using first parameter values of the model and a second image predicted using second parameter values of the model, and to minimize a difference between the second image and a ground truth image. During a first iteration of the training process the first image is predicted and during a second iteration the second image is predicted. The first parameter values are obtained during the first iteration by minimizing a difference between the first image and the ground truth image, and the second parameter values are obtained during the second iteration by maximizing the difference between the first image and the second image.

Abstract: An X-ray imaging apparatus includes an imaging device configured to capture a camera image of a target; a controller configured to stitch a plurality of X-ray images of respective divided regions of the target to generate one X-ray image of the target; and a display configured to display a settings window that provides a GUI for receiving a setting of an X-ray irradiation condition for the respective divided regions, and display the camera image in which positions of the respective divided regions are displayed.

Abstract: A radiographic imaging system having a number of useable digital radiographic detectors selectively activates one of the detectors to capture a radiographic image. The DR detectors each include a movement detector configured to transmit a movement signal when a movement of the corresponding detector is sensed. A portion of the system is disabled in response to receiving a movement signal transmitted from a motion sensor in a detector other than the active detector.

Abstract: An analysis method is for automatically determining radiological result data from radiology data sets. In an embodiment, the method includes provisioning a first radiology data set at least based on X-ray data of a first X-ray energy spectrum; provisioning at least one second radiology data set at least based on X-ray data of a second X-ray energy spectrum; provisioning an analysis unit including a neural network, to analyze radiology data sets including an input layer with a plurality of cells, a number of intermediate layers and an output layer representing a radiological result; analyzing the first radiology data set and the at least one second radiology data set, at least subsets of the first radiology data set and the at least one second radiology data set being assigned to different cells of the input layer for joint processing in the neural network; and acquiring result data on the output layer.

Abstract: Methods and apparatus for computer-aided prostate condition diagnosis are disclosed. An example computer-aided prostate condition diagnosis apparatus includes memory to store instructions and a processor. The example processor can detect a lesion from an image of a prostate gland and generate a mapping of the lesion from the image to a sector map, the generating the mapping of the lesion comprising identifying a depth region of the lesion, wherein the depth region indicates a location of the lesion along a depth axis. The processor can also provide the sector map comprising a representation of the lesion within the prostate gland mapped from the image to the sector map.

Abstract: Provided is a dental image registration device comprising: an outermost boundary detection unit for detecting, from first teeth image data, a first outermost boundary region which is the outermost boundary region of dentition, and detecting, from second teeth image data, a second outermost boundary region which is the outermost boundary region of the dentition; and an image registration unit which registers the first and second teeth image data on the basis of a first inscribed circle inscribed within the first outermost boundary region and a second inscribed circle inscribed within the outermost boundary region, or registers the first and second teeth image data on the basis of a first central point of the first outermost boundary region and a second central point of the second outermost boundary region.

Abstract: A method is disclosed for generating an image. An embodiment of the method includes detecting a first projection data set via a first group of detector units, the first group including a first plurality of first detector units, each having more than a given number of detector elements; detecting a second projection data set via a second group of detector units, the second group including a second plurality of second detector units, each including, at most, the given number of detector elements; reconstructing first image data based on the first projection data set; reconstructing second image data based on the second projection data set; and combining the first image data and the second image data. A non-transitory computer readable medium, a data processing unit, and an imaging device including the data processing unit are also disclosed.

Abstract: Systems and methods for determining a target region of interest (ROI) for image registration is provided. The methods may include obtaining a target image of a subject including a target portion of the subject and obtaining feature information related to a movement of one or more feature portions of the subject. The methods may further include identifying, from the one or more feature portions, one or more reference portions of the subject based on the feature information. A position of the target portion of the subject is unrelated to a movement of the one or more reference portions. The methods may further include determining, in the target image, the target ROI based on corresponding feature information related to the one or more reference portions.

Abstract: Radiation beam alignment for a LINAC including (1) for each beam alignment parameter value of a set: (a) with a beam alignment parameter of a LINAC set to the beam alignment parameter value, using a gantry to generate a radiation beam; (b) using an imaging device to acquire a radiation transmission image indicative of a radiation field of the radiation beam after passing by a radiation opaque marker; (c) determining a location of a beam axis of the radiation beam and a center of a shadow of the marker based on the radiation transmission image; and (d) determining a target-to-beam-axis distance between the location of the beam axis and the center of the shadow of the radiation opaque marker; and (2) determining an optimum beam alignment parameter value based on the beam alignment parameter values and the target-to-beam-axis distances determined with the LINAC set to the beam alignment parameter values.

Abstract: For training a machine learning system for representing a patient body a plurality of stored medical imaging data sets each representing at least a part of a respective patient are obtained. A first one of the plurality of stored medical imaging data sets represents a different part of the patient body than a second one of the plurality of stored medical imaging data sets. A plurality of landmarks in the stored medical imaging data sets are estimated, and each of the stored medical imaging data sets are aligned to a predefined pose using the plurality of landmarks. A plurality of points in the aligned medical imaging data sets are sampled, and the machine learning system is trained based on at least the plurality of points. The learned parameters of the machine learning system are then stored and used in a method for inferring a body representation.

Abstract: Computer systems and computer-implemented analysis methods may be used for assistance in planning and/or performing a medical procedure, such as percutaneous nephrolithotomy or percutaneous nephrolithotripsy. The method may include receiving one or more radiographic images of an anatomical structure of a patient, generating a display of the radiographic image(s), generating at least one request for user input to identify features of the anatomical structure, receiving user input identifying the features of the anatomical structure, identifying at least one access plan based on the received user input, and generating a display of the identified access plan(s) associated with the radiographic image(s). The method may include generating a patient template that indicates an insertion site according to the identified access plan(s).

Abstract: Various methods and systems are provided for mammogram imaging. In one example, a method for an x-ray mammography system includes determining, based on a pre-exposure image of a subject acquired with the x-ray mammography system before acquisition of one or more main images, that the subject has an implant; automatically adjusting one or more imaging parameters in response to determining that the subject has the implant; and acquiring the one or more main images with the adjusted one or more imaging parameters.