117 research outputs found
A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis
Breast ultrasound images have several attractive properties that make them an interesting tool in breast cancer detection. However, their intrinsic high noise rate and low contrast turn mass detection and segmentation into a challenging task. In this article, a fully automated two-stage breast mass segmentation approach is proposed. In the initial stage, ultrasound images are segmented using support vector machine or discriminant analysis pixel classification with a multiresolution pixel descriptor. The features are extracted using non-linear diffusion, bandpass filtering and scale-variant mean curvature measures. A set of heuristic rules complement the initial segmentation stage, selecting the region of interest in a fully automated manner. In the second segmentation stage, refined segmentation of the area retrieved in the first stage is attempted, using two different techniques. The AdaBoost algorithm uses a descriptor based on scale-variant curvature measures and non-linear diffusion of the original image at lower scales, to improve the spatial accuracy of the ROI. Active contours use the segmentation results from the first stage as initial contours. Results for both proposed segmentation paths were promising, with normalized Dice similarity coefficients of 0.824 for AdaBoost and 0.813 for active contours. Recall rates were 79.6% for AdaBoost and 77.8% for active contours, whereas the precision rate was 89.3% for both methods.info:eu-repo/semantics/publishedVersio
Investigating the role of machine learning and deep learning techniques in medical image segmentation
openThis work originates from the growing interest of the medical imaging community in the application of
machine learning techniques and, from deep learning to improve the accuracy of cancerscreening. The thesis
is structured into two different tasks.
In the first part, magnetic resonance images were analysed in order to support clinical experts in the
treatment of patients with brain tumour metastases (BM). The main topic related to this study was to
investigate whether BM segmentation may be approached successfully by two supervised ML classifiers
belonging to feature-based and deep learning approaches, respectively. SVM and V-Net Convolutional Neural
Network model are selected from the literature as representative of the two approaches.
The second task related to this thesisis illustrated the development of a deep learning study aimed to process
and classify lesions in mammograms with the use of slender neural networks. Mammography has a central
role in screening and diagnosis of breast lesions. Deep Convolutional Neural Networks have shown a great
potentiality to address the issue of early detection of breast cancer with an acceptable level of accuracy and
reproducibility. A traditional convolution network was compared with a novel one obtained making use of
much more efficient depth wise separable convolution layers.
As a final goal to integrate the system developed in clinical practice, for both fields studied, all the Medical
Imaging and Pattern Recognition algorithmic solutions have been integrated into a MATLAB® software
packageopenInformatica e matematica del calcologonella gloriaGonella, Glori
Enhanced algorithms for lesion detection and recognition in ultrasound breast images
Mammography is the gold standard for breast cancer detection. However, it has very
high false positive rates and is based on ionizing radiation. This has led to interest in
using multi-modal approaches. One modality is diagnostic ultrasound, which is based
on non-ionizing radiation and picks up many of the cancers that are generally missed
by mammography. However, the presence of speckle noise in ultrasound images has a
negative effect on image interpretation. Noise reduction, inconsistencies in capture
and segmentation of lesions still remain challenging open research problems in
ultrasound images.
The target of the proposed research is to enhance the state-of-art computer vision
algorithms used in ultrasound imaging and to investigate the role of computer
processed images in human diagnostic performance. [Continues.
Novel Deep Learning Models for Medical Imaging Analysis
abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
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