Medical Image Segmentation Using Machine Learning

Image segmentation is the most crucial step in image processing and analysis. It can divide an image into meaningfully descriptive components or pathological structures. The result of the image division helps analyze images and classify objects. Therefore, getting the most accurate segmented image is essential, especially in medical images. Segmentation methods can be divided into three categories: manual, semiautomatic, and automatic. Manual is the most general and straightforward approach. Manual segmentation is not only time-consuming but also is imprecise. However, automatic image segmentation techniques, such as thresholding and edge detection, are not accurate in the presence of artifacts like noise and texture. This research aims to show how to extract features and use traditional machine learning methods like a random forest to obtain the most accurate regions of interest in CT images. In addition, this study shows how to use a deep learning model to segment the wound area in raw pictures and then analyze the corresponding area in near-infrared images. This thesis first gives a brief review of current approaches to medical image segmentation and deep learning background. Furthermore, we describe different approaches to build a model for segmenting CT-Scan images and Wound Images. For the results, we achieve 97.4% accuracy in CT-image segmentation and 89.8% F1-Score For wound image segmentation

A deep learning approach to bone segmentation in CT scans

This thesis proposes a deep learning approach to bone segmentation in abdominal CT scans. Segmentation is a common initial step in medical images analysis, often fundamental for computer-aided detection and diagnosis systems. The extraction of bones in CT scans is a challenging task, which if done manually by experts requires a time consuming process and that has not today a broadly recognized automatic solution. The method presented is based on a convolutional neural network, inspired by the U-Net and trained end-to-end, that performs a semantic segmentation of the data. The training dataset is made up of 21 abdominal CT scans, each one containing between 403 and 994 2D transversal images. Those images are in full resolution, 512x512 voxels, and each voxel is classified by the network into one of the following classes: background, femoral bones, hips, sacrum, sternum, spine and ribs. The output is therefore a bone mask where the bones are recognized and divided into six different classes. In the testing dataset, labeled by experts, the best model achieves a Dice coefficient as average of all bone classes of 0.93. This work demonstrates, to the best of my knowledge for the first time, the feasibility of automatic bone segmentation and classification for CT scans using a convolutional neural network

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome