Abstract: A system and method generating an optimized medical image using a machine learning model are provided. The method includes (i) receiving one or more medical images, (ii) segmenting to generate a transformed medical image for detecting a plurality of target elements, (iii) displaying the transformed medical image, (iv) receiving markings and scribblings associated with scribble locations from a user, (v) identifying errors associated with an outline of a target element, (vi) computing a loss function for a location of pixels where the target element is located on the transformed medical image, (vii) modifying the pre-defined weights (w) to match the segmentation output and the determined target element, (viii) determining whether the segmentation output is matched with the target element and (ix) generating the optimized medical image if the segmentation output is matched with the determined target element.

Abstract: A system and method for generating an optimized image with scribble-based interactive image segmentation model using a machine learning are provided. The method includes, (i) segmenting, using a machine learning model, an image to classify into classes each class is represented with a label, (ii) displaying the classified image which specifies the classes on the classified image with outlines, (iii) enabling a user to scribble on the classified image to annotate the classes if an area is not classified, (iv) assigning a color mask for each scribbled area, (v) computing, using the machine learning model, a loss function for a location of pixels based on color mask, (vi) modifying pre-defined weights for each scribbled area to match the annotated image and a determined class on the classified image, and (vii) generating the optimized image if the annotated image is matched with the determined class on the classified image.

Abstract: A system and method generating an optimized medical image using a machine learning model are provided. The method includes (i) receiving one or more medical images, (ii) segmenting to generate a transformed medical image for detecting a plurality of target elements, (iii) displaying the transformed medical image, (iv) receiving markings and scribblings associated with scribble locations from a user, (v) identifying errors associated with an outline of a target element, (vi) computing a loss function for a location of pixels where the target element is located on the transformed medical image, (vii) modifying the pre-defined weights (w) to match the segmentation output and the determined target element, (viii) determining whether the segmentation output is matched with the target element and (ix) generating the optimized medical image if the segmentation output is matched with the determined target element.