Abstract: Methods and systems for automated target and tissue segmentation using multi-modal imaging and ensemble machine learning models are provided herein. In some embodiments, a method comprises: receiving a plurality of medical images, wherein each of the plurality of medical images includes a target and normal tissue; combining the plurality of medical images to align the target and normal tissue across the plurality of medical images; inputting the combined medical images into each of a plurality of machine learning models; receiving, in response to the input, an output from each of the plurality of machine learning models; combining the results of the plurality of machine learning models; generating a final segmentation image based on the combined results of the plurality of machine learning models; assigning a score to each segmented target and normal tissue; and sorting the segmented targets and normal tissues based on the scores.

Abstract: Methods and systems for automated target and tissue segmentation using multi-modal imaging and ensemble machine learning models are provided herein. In some embodiments, a method comprises: receiving a plurality of medical images, wherein each of the plurality of medical images includes a target and normal tissue; combining the plurality of medical images to align the target and normal tissue across the plurality of medical images; inputting the combined medical images into each of a plurality of machine learning models; receiving, in response to the input, an output from each of the plurality of machine learning models; combining the results of the plurality of machine learning models; generating a final segmentation image based on the combined results of the plurality of machine learning models; assigning a score to each segmented target and normal tissue; and sorting the segmented targets and normal tissues based on the scores.

Abstract: Methods and systems for automated target and tissue segmentation using multi-modal imaging and ensemble machine learning models are provided herein. In some embodiments, a method comprises: receiving a plurality of medical images, wherein each of the plurality of medical images includes a target and normal tissue; combining the plurality of medical images to align the target and normal tissue across the plurality of medical images; inputting the combined medical images into each of a plurality of machine learning models; receiving, in response to the input, an output from each of the plurality of machine learning models; combining the results of the plurality of machine learning models; generating a final segmentation image based on the combined results of the plurality of machine learning models; assigning a score to each segmented target and normal tissue; and sorting the segmented targets and normal tissues based on the scores.

Abstract: Methods and systems for automated target and tissue segmentation using multi-modal imaging and ensemble machine learning models are provided herein. In some embodiments, a method comprises: receiving a plurality of medical images, wherein each of the plurality of medical images includes a target and normal tissue; combining the plurality of medical images to align the target and normal tissue across the plurality of medical images; inputting the combined medical images into each of a plurality of machine learning models; receiving, in response to the input, an output from each of the plurality of machine learning models; combining the results of the plurality of machine learning models; generating a final segmentation image based on the combined results of the plurality of machine learning models; assigning a score to each segmented target and normal tissue; and sorting the segmented targets and normal tissues based on the scores.

Abstract: A computer implemented method and apparatus for processing images comprises obtaining two or more images acquired by ultrasound. One or more operating parameters of the ultrasound probe (transducer) are varied so that the resulting images of a material or object under investigation differ with respect to intensity. Examples of parameters which may be varied include probe angle, frequencies, and even the time and/or resolution of the respective images. The method further comprises creating a new image by selectively subtracting one or more images from one or more others. In an embodiment, there are two images and one is partially subtracted from the other. If negative values are obtained as a result of the subtraction, such values are set to zero.

Abstract: A computer implemented method and apparatus for processing images comprises obtaining two or more images acquired by ultrasound. One or more operating parameters of the ultrasound probe (transducer) are varied so that the resulting images of a material or object under investigation differ with respect to intensity. Examples of parameters which may be varied include probe angle, frequencies, and even the time and/or resolution of the respective images. The method further comprises creating a new image by selectively subtracting one or more images from one or more others. In an embodiment, there are two images and one is partially subtracted from the other. If negative values are obtained as a result of the subtraction, such values are set to zero.