An approach for training, on a computer, one or more deep learning algorithms with a plurality of mammograms with known outcomes based, at least in part, on using a set of mammograms of each patient in the plurality of mammograms. The approach includes receiving a first set of mammograms of a first patient. The first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient. The approach includes the trained convolutional neural network extracting the set of features from each mammogram of the set of mammograms of the first patient. Furthermore, the approach includes using a second deep learning algorithm of the one or more deep learning algorithms to perform an evaluation of the unevaluated mammogram of the first patient based, at least in part, on the set of prior mammograms of the first patient.

The present disclosure provides an operating method of an automatic analysis system on magnetic resonance imaging (MRI), which includes steps as follows. Images are received from of the subject's brain from the MRI machine. Contrast-enhanced T1-weighted images and T2-weighted images are obtained from the images, and the pre-processing is performed on the images. The ratio of T2-weighted images to contrast-enhanced T1-weighted images is calculated to generate contrast-enhanced images. The unsupervised clustering is performed on the region of interest in the contrast-enhanced image to separate a cystic part and a non-cystic part so as to calculate the feature parameters. After radiosurgery is performed on the brain tumor corresponding to the region of interest, the volume change of the tumor is analyzed. The linear regression analysis of the feature parameters and the volume change of the tumor is performed for prognostic evaluation.

The present disclosure provides methods for automated slide assessments made in conjunction with digital image-based microscopy. Automated methods of acquiring patient information and specimen information from prepared slides, and digitally linking such information into patient-tagged specimen data, are provided. Also provided are methods that include automatically identifying an optimal area for morphological assessment of a blood smear on a hematological slide, including methods for triggering the analysis of such an area, e.g., using an automated digital image-based hematology system. The present disclosure also provides devices, systems and computer readable media for use in performing processes of the herein described methods.

A computer-implemented method for tracking surgical textiles includes receiving a first image comprising a first textile-depicting image region, receiving a second image comprising a second textile-depicting image region, measuring a likelihood that the first and second image regions depict at least a portion of the same textile, and incrementing an index counter if the measure of likelihood does not meet a predetermined threshold. The measure of likelihood may be based on at least one classification feature at least partially based on aspects or other features of the first and second images.

A method for tracking movement of nematode worms comprises: (i) providing a plurality of worms on a translucent substrate; (ii) obtaining a first image of a field of view including the plurality of worms by transmission imaging; (iii) obtaining a first difference image of the plurality of worms corresponding to an intensity difference between said first image and a background image of the field of view; (iv) repeating the following steps (a) to (d) a plurality of N times, for n=1 to N: (a) determining, from the first difference image, an nth pixel corresponding to a maximum intensity difference; (b) selecting, from the first difference image, an nth block of pixels comprising the selected nth pixel; (c) determining a coordinate associated with the selected nth block of pixels; and (d) updating said first difference image by setting each pixel of said nth block of pixels in said first difference image to a value corresponding to a zero or low intensity difference; (v) obtaining a sequence of M subsequent images of the field of view by transmission imaging; and (vi) repeating the following steps (f) and (g) for each of the M subsequent images, for m=2 to m=M+1: (f) obtaining an mth difference image of the plurality of worms corresponding to an intensity difference between the mth subsequent image and said background image; and (g) repeating the following steps a plurality of N times, for n=1 to n=N: determining, from the mth difference image, an nth pixel corresponding to a maximum intensity difference or a centre of the intensity difference distribution of a trial block of pixels positioned at the determined coordinate associated with the corresponding nth selected block of pixels of the (m−1)th difference image; selecting an nth block of pixels of said mth difference image, said nth block of pixels comprising the determined nth pixel; and determining a coordinate associated with the selected nth block of pixels of said mth difference image.

Examples of monitoring of follicular fluid flow using image processing are described herein. Image frames pertaining to a pre-defined target area defined for a collection tube during an ovum pick-up (OPU) step of an in-vitro fertilization (IVF) procedure may be obtained in real-time. The target area corresponds to a region indicating entry of a follicular fluid into the collection tube. Thereafter, the image frames may be processed to detect occurrence of a flow of the follicular fluid as well as presence of blood in the follicular fluid. Accordingly, a first alert signal indicating detection of occurrence of flow of the follicular fluid and a second alert signal indicating presence of blood in the follicular fluid, may be generated to notify a medical practitioner.

An analysis apparatus according to an embodiment includes processing circuitry. The processing circuitry performs registration between first ultrasound image data obtained at a first phase by an ultrasound diagnostic apparatus and first medical image data obtained by a medical image diagnostic apparatus other than the ultrasound diagnostic apparatus and performs registration between the first ultrasound image data and second ultrasound image data obtained at a second phase different from the first phase by the ultrasound diagnostic apparatus, to generate second medical image data registered with the second ultrasound image data; and combines the second ultrasound image data and the second medical image data to generate a single image, thereby performing registration between ultrasound image data by the ultrasound diagnostic apparatus and medical image data by the medical image diagnostic apparatus.

An ultrasound imaging system and method includes acquiring ultrasound image data while moving an ultrasound probe, automatically identifying a plurality of segments of interest in the ultrasound image data, automatically applying temporal scaling to at least one of the plurality of segments of interest, and displaying the ultrasound image data as a panoramic view comprising a plurality of videos, where each of the plurality of videos is based on a different one of the plurality of the segments of interest, and where, based on the temporal scaling, each of the plurality of videos in the panoramic view takes the same amount of time to play.

There is provided a medical information acquisition device including an information acquisition unit that acquires functional change information obtained on the basis of a reference image and a past image acquired by capturing images of the same subject at a reference time and a past time closer to the past than the reference time, respectively, using a trained model.

A deep machine-learning approach is used for medical image fusion by a medical imaging system. This one approach may be used for different applications. For a given application, the same deep learning is used but with different application-specific training data. The resulting deep-learnt classifier provides a reduced feature vector in response to input of intensities of one image and displacement vectors for patches of the one image relative to another image. The output feature vector is used to determine the deformation for medical image fusion.