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.

This invention provides a method applied for the new dynamic arc radiotherapy treatment planning to calculate an optimal arc angle. With this invention, an operator without rich experience is able to reach the expected low dose in lungs easily and quickly. This invention can not only estimate the distribution of low radiation dose in lungs but also reduce the shortcomings like consumption of time and inaccuracy caused by manual trial and error.

Provided are robotic systems and methods for navigation of luminal network that detect physiological noise. In one aspect, the system includes a set of one or more processors configured to receive first and second image data from an image sensor located on an instrument, detect a set of one or more points of interest the first image data, and identify a set of first locations and a set of second location respectively corresponding to the set of points in the first and second image data. The set of processors are further configured to, based on the set of first locations and the set of second locations, detect a change of location of the instrument within a luminal network caused by movement of the luminal network relative to the instrument based on the set of first locations and the set of second locations.

A medical image visualization apparatus includes a reception interface configured to receive at least one medical image; at least one processor; and a display. The at least one processor is configured to recognize a connection image that is presented on the at least one medical image and connects the at least one medical image and detailed analysis information regarding the at least one medical image; acquire the detailed analysis information, connected to the at least one medical image by the connection image; and visualize the detailed analysis information on the display. The detailed analysis information includes an intermediate result and a final result of a first process in which an image analysis result of an original medical image is generated as the at least one medical image.

A new chest X-ray database, referred to as “ChestX-ray8”, is disclosed herein, which comprises over 100,000 frontal view X-ray images of over 32,000 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. We demonstrate that these commonly occurring thoracic diseases can be detected and spatially-located via a unified weakly supervised multi-label image classification and disease localization framework, which is validated using our disclosed dataset.

The embodiments disclose an ultrasound system comprising: a probe configured to obtain ultrasound data relating to a scanning region including at least part of a pleural interface of a lung; and a data analyzer, configured to automatically detect information for determining lung sliding and/or lung point using one or more cross correlation maps derived from the data. The embodiments also disclose a method thereof.

The invention discloses an unsupervised content-preserved domain adaptation method for multiple CT lung texture recognition, which belongs to the field of image processing and computer vision. This method enables the deep network model of lung texture recognition trained in advance on one type of CT data (on the source domain), when applied to another CT image (on the target domain), under the premise of only obtaining target domain CT image and not requiring manually label the typical lung texture, the adversarial learning mechanism and the specially designed content consistency network module can be used to fine-tune the deep network model to maintain high performance in lung texture recognition on the target domain. This method not only saves development labor and time costs, but also is easy to implement and has high practicability.

A controller (522) for live annotation of interventional imagery includes a memory (52220) that stores software instructions and a processor (52210) that executes the software instructions. When executed by the processor (52210), the software instructions cause the controller (522) to implement a process that includes receiving (S210) interventional imagery during an intraoperative intervention and automatically analyzing (S220) the interventional imagery for detectable features. The process executed when the processor (52210) executes the software instructions also includes detecting (S230) a detectable feature and determining (S240) at add an annotation to the interventional imagery for the detectable feature. The processor further includes identifying (S250) a location for the annotation as an identified location in the interventional imagery and adding (S260) the annotation to the interventional imagery at the identified location to correspond to the detectable feature. During the intraoperative intervention, a video is output (S270) as video output based on interventional imagery and the annotation, including the annotation overlaid on the interventional imagery at the identified location.

The present invention relates to a method for estimating lung morphometry based on aerosol deposition characteristics using an imaging means such as a gamma camera to scan the lungs. An adaptive image threshold technique is used to determine the ratio of deposition in central to peripheral region of the lung (C/P ratio). The morphometric parameters such as length and diameter of distal lung airways (P.sub.8 and P.sub.9 respectively) and mean alveolar diameter (d.sub.alv) are determined from aerosol retention data and clearance data.

An ear nose and throat (ENT) imaging and analysis system includes an endoscope usable to capture images of the nasal canal and other aspects of patient anatomy. Endoscopic images may be presented to a user via a touchscreen display, and the software may provide different imaging modes that aid in identifying particular anatomical structures or areas within the nasal canal. In one mode, the system uses an object recognition process to identify the nasal valve opening within the images at a relaxed state, and during forceful inhalation, and then calculates the difference between the two states, which may be suggestive of nasal valve collapse. In other modes, the system is configured to identify abnormalities of the inferior turbinate, septum, or other anatomy, as well as empty spaces within the nasal canal, as well as areas and volumes of empty space and user defined boundaries.