Koninklijke Philips Corporation N.v.

Subhendu Seth

Pallavi Vajinepalli

Celine Firtion

David N Roundhill

Karthik Krishnan

Payal Keswarpu

Sarif Kumar Naik

Soumabha Bhowmick

Sunil Kumar

Shankar Mosur Venkatesan

Vipin Gupta

Ganesan Ramachandran

Lu Wang

Matthew Rielly

Sindhu Priyadarshini Nellur Prakash

Anumod Odungattu Thodiyil

Sanjay Jayavanth

Giridhar Narasapura Rajagopalaiah

Sanjay Ramachandra Hegde

Srikanth Shettigar

Ishank Jain

Karthik Raj Katipally

Keswarpu Payal

Achyuta Aich

A mechanism for automatically generating and ranking M-mode lines for generating or defining M-mode data usable to assess fetal heart activity, e.g. determine a fetal heart rate. A region of interest, containing a fetal heart, in a sequence of ultrasound images is identified. The region of interest is used to define the position of each of a plurality of M-mode lines, e.g. anatomical M-mode lines. A quality measure of each M-mode line is determined based on M-mode data generated for each M-mode line, and the quality measures are then used to rank the M-mode lines.

Generation of M-mode data for detecting fetal cardiac activity

The invention relates to a computer-implemented method for automatically detecting anatomical structures () in a medical image () of a subject, the method comprising applying an object detector function () to the medical image, wherein the object detector function performs the steps of: (A) applying a first neural network () to the medical image, wherein the first neural network is trained to detect a first plurality of classes of larger-sized anatomical structures (), thereby generating as output the coordinates of at least one first bounding box () and the confidence score of it containing a larger-sized anatomical structure; (B) cropping () the medical image to the first bounding box, thereby generating a cropped image () containing the image content within the first bounding box (); and (C) applying a second neural network () to the cropped medical image, wherein the second neural network is trained to detect at least one second class of smaller-sized anatomical structures (), thereby generating as output the coordinates of at least one second bounding box () and the confidence score of it containing a smaller-sized anatomical structure.

Method and system for automatically detecting anatomical structures in a medical image

A computer-implemented method for visualization of an elongated anatomical structure (), for example of a fetal spine using ultrasound is provided. The method comprising the steps of: receiving a plurality of 3D ultrasound image volumes, each image volume depicting at least a portion of an elongated anatomical structure (); on each 3D ultrasound image volume, automatically or semi-automatically fitting a parametric curve () to the depicted portion of the elongated anatomical structure, the parametric curve being defined by curve parameters; reformatting each 3D ultrasound image volume by applying a transformation which straightens the parametric curve along at least one axis, so as to generate a plurality of reformatted image volumes and reformatted parametric curves (); registering the reformatted image volumes with one another by determining the joining point of their respective parametric curves; and fusing the reformatted image volumes with one another to yield a fused image depicting the whole elongated anatomical structure or a larger portion thereof than the 3D ultrasound image volumes.

Computer-implemented method for visualization of an elongated anatomical structure

The invention provides for a method of obtaining a composite 3D ultrasound image of a region of interest. The method includes obtaining preliminary ultrasound data from a region of interest of a subject and identifying an anatomical feature within the region of interest based on the preliminary ultrasound data. A first imaging position and one or more additional imaging positions are then determined based on the anatomical feature. A first 3D ultrasound image is obtained from the first imaging position and one or more additional 3D ultrasound images are obtained from the one or more additional imaging positions, wherein a portion of the first 3D ultrasound image overlaps a portion of the one or more additional 3D ultrasound images, thereby forming an overlapping portion comprising the anatomical feature. Spatial registration is performed between the first 3D ultrasound image and the one or more additional 3D ultrasound images based on the anatomical feature the 3D ultrasound images are then blended based on the spatial registration, thereby generating a composite 3D ultrasound image.

Methods and systems for acquiring composite 3D ultrasound images

The present invention relates to a device () for detecting a misuse of a medical imaging system (), comprising a data interface () for acquiring medical image data () and audit log data () from the medical imaging system (); a processing unit () which is configured to configured to analyse the medical image data () to determine whether or not a part of a fetus is imaged in the medical image data (), to compare the medical image data () and the audit log data () with each other, and to determine based on said comparison whether there is a mismatch between the medical image data () and the audit log data (); and a feedback unit () which is configured to generate a misuse alert signal if a mismatch is detected by the processing unit ().

Device and method for detecting misuse of a medical imaging system

The present invention relates to a device () and a method () for determining at least one final two-dimensional image or slice for visualizing an object of interest in a three-dimensional ultrasound volume. The method () for determining at least one final two-dimensional image, the method comprises the steps: a) providing () a three-dimensional image of a body region of a patient body, wherein an applicator configured for fixating at least one radiation source is inserted into the body region; b) providing () an initial direction, in particular by randomly determining the initial direction within the three-dimensional image; c) repeating () the following sequence of steps s1) to s4): s1) determining (), via a processing unit, a set-direction within the three-dimensional image based on the initial direction for the first sequence or based on a probability map determined during a previous sequence; s2) extracting (), via the processing unit, an image-set of two-dimensional images from the three-dimensional image, such that the two-dimensional images of the image-set are arranged coaxially and subsequently in the set-direction; s3) applying (), via the processing unit, an applicator pre-trained classification method to each of the two-dimensional images of the image-set resulting in a probability score for each of the two-dimensional images of the image-set indicating a probability of the applicator being depicted, in particular fully depicted, in the respective two-dimensional image of the image-set in a cross-sectional view; and s4) determining (), via the processing unit, a probability-map representing the probability scores of the two-dimensional images of the image-set with respect to the set-direction; wherein the method comprises the further step: d) determining (), via a processing unit and after finishing the last sequence, the two-dimensional image associated with the highest probability score, in particular from the image-set determined during the last sequence, as the final two-dimensional image. The invention provides an efficient way to ensure that the ultrasound volume has the required clinical information by providing the necessary scan planes having the object of interest e.g. the applicator () in a three-dimensional ultrasound volume.

Determining at least one final two-dimensional image for visualizing an object of interest in a three dimensional ultrasound volume

The invention relates to a method and an apparatus for cervical image analysis, wherein a transformation zone is identified in an acetic acid image by registering with its Lugol's iodine counterpart. Then, regions in the transformation zone which show significant changes in whiteness are identified as aceto-white regions and registered with the corresponding Lugol's iodine image, and it is determined if the identified regions in the Lugol's iodine image are iodine negative or positive. Based thereon, the aceto-white region can be categorized as one of metaplasia, inflammation or premalignant lesion region.

Method and apparatus for cervical image analysis with improved reliability

The invention provides a system () for acquiring cervical images which comprises an image acquisition subsystem () for acquiring cervical images () of a cervical region of a patient during a colposcopy procedure, and a display subsystem () for displaying the cervical images on a display () by providing image data () of the cervical images to the display. According to the invention, the image acquisition subsystem () is arranged for, when operating in an interval mode, acquiring the cervical images () at predetermined time intervals to obtain a time-series of cervical images showing changes in the cervical region over time, and reporting a progress of said acquiring to the display subsystem. Moreover, the display subsystem () is arranged for establishing a progress indicator (-) on the display by generating indicator data () and providing the indicator data to the display (), the progress indicator providing visual feedback on the progress of said acquiring to a user. Accordingly, the user is provided with visual feedback on a status of the acquiring of the cervical images. In particular, the progress indicator avoids that the user experiences a loss of control or confusion.

Acquiring cervical images

A method or system detects abnormalities in a biological sample cells. The method or system performs at least the following steps, or is capable to carry out at least the following steps, respectively: a) acquiring an image of the sample by digital image acquisition, b) optionally, carrying out digital image processing, c) selecting a field of view, d) determining, by digital image processing, whether or not, in the field of view, cell aggregates exist, and e1) selecting a new field of view and carrying on with step c) if, in step d), it turns out that, in the field of view, the determination of cell aggregates is negative, or e2) carrying out further process steps if in step d), it turns out that, in the field of view, the determination of cell aggregates is affirmative.

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Bengaluru, India

Subhendu Seth