A New Model for Image Segmentation Based on Deep Learning

Abstract

Image segmentation is main point in computer vision (CV) and image processing (IP), that are used routinely in the fields of medicine and surgery training tools. Segmenting images and converting into a model that depends on work by the different algorithms from analysis DICOM files to convert to three-dimensional models. This paper describes a combination of two fields of solving segmentation problem to convert through the workflow of a hybrid algorithm structure Convolutional neural network (CNN, Active Contour & Deep Multi-Planar) based on seg3d2 to switch DICOMmedical rays “Digital Imaging and Communications in Medicine” into a 3Dimintional model, using data from active contour to be the input of deep learning. the result of the pre-processing from DICOMrawimages, each image contains edges and image size =256 X 256 pixel, which through adjustment and control we can create multiple results for output using Active Contour, by resizing the threshold frames and gray-scale image, and show liver 3D-model Deep architecture, it is through the CNN which the images of the three axes X, Y, and Z (three orthogonal) (coronal = X, sagittal = Y, axial = Z = 1) are determined and matched with a real image of the body, the area required to be determined, and edits the contrast using a histogram. This research will be using are human liver DICOM images and is divided into two stages (medical image segmentation - retinal model optimization), to help surgeons to study the patient’s condition with accuracy and efficiency through the use ofmixed reality technology in liver surgery [living donor liver transplantation (LDLT)], all implement by Seg3D2 and Python.