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DEEP LEARNING FOR IMAGE PROCESSING WITH APPLICATIONS TO MEDICAL IMAGING
Title
DEEP LEARNING FOR IMAGE PROCESSING WITH APPLICATIONS TO MEDICAL IMAGING
Creator
Zarshenas, Amin
Date
2019
Description
Deep Learning is a subfield of machine learning concerned with algorithms that learn hierarchical data representations. Deep learning has...
Show moreDeep Learning is a subfield of machine learning concerned with algorithms that learn hierarchical data representations. Deep learning has proven extremely successful in many computer vision tasks including object detection and recognition. In this thesis, we aim to develop and design deep-learning models to better perform image processing and tackle three important problems: natural image denoising, computed tomography (CT) dose reduction, and bone suppression in chest radiography (“chest x-ray”: CXR). As the first contribution of this thesis, we aimed to answer to probably the most critical design questions, under the task of natural image denoising. To this end, we defined a class of deep learning models, called neural network convolution (NNC). We investigated several design modules for designing NNC for image processing. Based on our analysis, we design a deep residual NNC (R-NNC) for this task. One of the important challenges in image denoising regards to a scenario in which the images have varying noise levels. Our analysis showed that training a single R-NNC on images at multiple noise levels results in a network that cannot handle very high noise levels; and sometimes, it blurs the high-frequency information on less noisy areas. To address this problem, we designed and developed two new deep-learning structures, namely, noise-specific NNC (NS-NNC) and a DeepFloat model, for the task of image denoising at varying noise levels. Our models achieved the highest denoising performance comparing to the state-of-the-art techniques.As the second contribution of the thesis, we aimed to tackle the task of CT dose reduction by means of our NNC. Studies have shown that high dose of CT scans can increase the risk of radiation-induced cancer in patients dramatically; therefore, it is very important to reduce the radiation dose as much as possible. For this problem, we introduced a mixture of anatomy-specific (AS) NNC experts. The basic idea is to train multiple NNC models for different anatomic segments with different characteristics, and merge the predictions based on the segmentations. Our phantom and clinical analysis showed that more than 90% dose reduction would be achieved using our AS NNC model.We exploited our findings from image denoising and CT dose reduction, to tackle the challenging task of bone suppression in CXRs. Most lung nodules that are missed by radiologists as well as by computer-aided detection systems overlap with bones in CXRs. Our purpose was to develop an imaging system to virtually separate ribs and clavicles from lung nodules and soft-tissue in CXRs. To achieve this, we developed a mixture of anatomy-specific, orientation-frequency-specific (ASOFS) expert deep NNC model. While our model was able to decompose the CXRs, to achieve an even higher bone suppression performance, we employed our deep R-NNC for the bone suppression application. Our model was able to create bone and soft-tissue images from single CXRs, without requiring specialized equipment or increasing the radiation dose.
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Fast mesh based reconstruction for cardiac-gated SPECT and methodology for medical image quality assessment
Title
Fast mesh based reconstruction for cardiac-gated SPECT and methodology for medical image quality assessment
Creator
Massanes Basi, Francesc
Date
2018
Description
In this work, we are studying two different subjects that are intricately connected. For the first subject we are considering tools to...
Show moreIn this work, we are studying two different subjects that are intricately connected. For the first subject we are considering tools to improve the quality of single photon emission computed tomography (SPECT) imaging. Currently, SPECT images assist physicians to evaluate perfusion levels within the myocardium, aide in the diagnosis of various types of carcinomas, and measure pulmonary function. The SPECT technique relies on injecting a radioactive material into the patient's body and then detecting the emitted radiation by means of a gamma camera. However, the amount of radioactive material that can be given to a patient is limited by the negative effects that the radiation will have on the patient's health. This causes SPECT images to be highly corrupted by noise. We will focus our work on cardiac SPECT, which adds the challenge of the heart's continuous motion during the acquisition process. First, we describe the methodology used in SPECT imaging and reconstruction. Our methodology uses a content adaptive model, which uses more samples on the regions of the body that we want to be reconstructed more accurately and less in other areas. Then we describe our algorithm and our novel implementation that lets us use the content adaptive model to perform the reconstruction. In this work, we show that our implementation outperforms the reconstruction method used for clinical applications. In the second subject we are evaluating tools to measure image quality in the context of medical diagnosis. In signal processing, accuracy is typically measured as the amount of similarity between an original signal and its reconstruction. This similarity is traditionally a numeric metric that does not take into account the intended purpose of the reconstructed images. In the field of medical imaging, a reconstructed image is meant to aid a physician to perform a diagnostic task. Therefore, the quality of the reconstruction should be measured by how much it helps to perform the diagnostic task. A model observer is a computer tool that aims to mimic the performance of human observer, usually a radiologist, at a relevant diagnosis task. In this work we present our linear model observer designed to automatically select the features needed to model a human observer response. This is a novelty from the model observers currently being used in the medical imaging field, which instead usually have ad-hoc chosen features. Our model observer dependents only on the resolution of the image, not the type of imaging technique used to acquire the image.
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