Medical Images Segmentation Operations

Abstract

Extracting various valuable medical information from head MRI and CT series is one of the most important and challenging tasks in the area of medical image analysis. Due to the lack of automation for many of these tasks, they require meticulous preprocessing from the medical experts. Nevertheless, some of these problems may have semi-automatic solutions, but they are still dependent on the person's competence. The main goal of our research project is to create an instrument that maximizes series processing automation degree. Our project consists of two parts: a set of algorithms for medical image processing and tools for its results interpretation. In this paper we present an overview of the best existing approaches in this field, as well the description of our own algorithms developed for similar tissue segmentation problems such as eye bony orbit and brain tumor segmentation based on convolutional neural networks. The investigation of performance of different neural network models for both tasks as well as neural ensembles applied to brain tumor segmentation is presented. We also introduce our software named "MISO Tool" which is created specifically for this type of problems. It allows tissues segmentation using pre-trained neural networks, DICOM pixel data manipulation and 3D reconstruction of segmented areas.

Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software

“…Compared to the ground truth, the segmented maxilla and mandible achieved dice similarity coefficient scores of 0.924 and 0.949, respectively. Although strenuous, Magnetic Resonance Imaging (MRI) segmentation of soft tissue has gained importance for VSP, as shown by Musatian S.A. et al [23], who presented solutions for orbit and brain tumor segmentation based on CNNs. One software that is used in this study for semi-automatic segmentation is Brainlab IPlan.…”