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.
Highlights
Modern ray diagnosis is at the stage of development, and completely different settings and methods are required for different organs: x-ray, MRI, CT, ultrasound are supplemented with invasive contrast methods
We came to the conclusion that while the segmentation tasks on different body parts may seem different, they may all be derived from a core solution based on the deep neural networks
We explored state-of-the-art solutions based on deep neural networks for brain tumor segmentation and created an ensemble to see if their performance can be improved and used for the brain segmentation task and for complicated head bony structures in general
Summary
Modern ray diagnosis is at the stage of development, and completely different settings and methods are required for different organs: x-ray, MRI, CT, ultrasound are supplemented with invasive contrast methods. We formulated the problem of segmentation of tumor processes in MRI images. To determine the volume and edge isolation of structures, the problem of determining the volume of bony orbits on a CT was singled out In this method the bone structures have a high contrast, the distance between slices is very small, and the method itself is widely distributed and takes little time, which allows to study a large data volume. We explored state-of-the-art solutions based on deep neural networks for brain tumor segmentation and created an ensemble to see if their performance can be improved and used for the brain segmentation task and for complicated head bony structures in general. We use the results of this research as a first step for creating a convenient and powerful instrument for all medical specialties
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