2,008 research outputs found
3D medical volume segmentation using hybrid multiresolution statistical approaches
This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZuâbi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations
Simultaneous multislice acquisition with multi-contrast segmented EPI for separation of signal contributions in dynamic contrast-enhanced imaging
We present a method to efficiently separate signal in magnetic resonance imaging (MRI) into a base signal S0, representing the mainly T1-weighted component without T2*-relaxation, and its T2*-weighted counterpart by the rapid acquisition of multiple contrasts for advanced pharmacokinetic modelling. This is achieved by incorporating simultaneous multislice (SMS) imaging into a multi-contrast, segmented echo planar imaging (EPI) sequence to allow extended spatial coverage, which covers larger body regions without time penalty. Simultaneous acquisition of four slices was combined with segmented EPI for fast imaging with three gradient echo times in a preclinical perfusion study. Six female domestic pigs, German-landrace or hybrid-form, were scanned for 11 minutes respectively during administration of gadolinium-based contrast agent. Influences of reconstruction methods and training data were investigated. The separation into T1- and T2*-dependent signal contributions was achieved by fitting a standard analytical model to the acquired multi-echo data. The application of SMS yielded sufficient temporal resolution for the detection of the arterial input function in major vessels, while anatomical coverage allowed perfusion analysis of muscle tissue. The separation of the MR signal into T1- and T2*-dependent components allowed the correction of susceptibility related changes. We demonstrate a novel sequence for dynamic contrast-enhanced MRI that meets the requirements of temporal resolution (Ît < 1.5 s) and image quality. The incorporation of SMS into multi-contrast, segmented EPI can overcome existing limitations of dynamic contrast enhancement and dynamic susceptibility contrast methods, when applied separately. The new approach allows both techniques to be combined in a single acquisition with a large spatial coverage
Trustworthy Deep Learning for Medical Image Segmentation
Despite the recent success of deep learning methods at achieving new
state-of-the-art accuracy for medical image segmentation, some major
limitations are still restricting their deployment into clinics. One major
limitation of deep learning-based segmentation methods is their lack of
robustness to variability in the image acquisition protocol and in the imaged
anatomy that were not represented or were underrepresented in the training
dataset. This suggests adding new manually segmented images to the training
dataset to better cover the image variability. However, in most cases, the
manual segmentation of medical images requires highly skilled raters and is
time-consuming, making this solution prohibitively expensive. Even when
manually segmented images from different sources are available, they are rarely
annotated for exactly the same regions of interest. This poses an additional
challenge for current state-of-the-art deep learning segmentation methods that
rely on supervised learning and therefore require all the regions of interest
to be segmented for all the images to be used for training. This thesis
introduces new mathematical and optimization methods to mitigate those
limitations.Comment: PhD thesis successfully defended on 1st July 2022. Examiners: Prof
Sotirios Tsaftaris and Dr Wenjia Ba
Lumbar disk 3D modeling from limited number of MRI axial slices
This paper studies the problem of clinical MRI analysis in the field of lumbar intervertebral disk herniation diagnosis. It discusses the possibility of assisting radiologists in reading the patients MRI images by constructing a 3D model for the region of interest using simple computer vision methods. We use axial MRI slices of the lumbar area. The proposed framework works with a very small number of MRI slices and goes through three main stages. Namely, the region of interest extraction and enhancement, inter-slice interpolation, and 3D model construction. We use the Marching Cubes algorithm to construct the 3D model of the the region of interest. The validation of our 3D models is based on a radiologistâs analysis of the models. We tested the proposed 3D model construction on 83 cases and We have a 95% accuracy according to the radiologist evaluation. This study shows that 3D model construction can greatly ease the task of the radiologist which enhances the working experience. This leads eventually to more accurate and easy diagnosis process
Rich probabilistic models for semantic labeling
Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere BeitrÀge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinÀren Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung
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
A comprehensive review towards segmentation and detection of cancer cell and tumor for dynamic 3D reconstruction
Automated cancer cell and tumor segmentation and detection for 3D modeling are still an unsolved research
problem in computer vision, image processing and pattern recognition research domains. Human body is
complex three-dimensional structure where numerous types of cancer and tumor may exist regardless of shape
or position. A three-dimensional (3D) reconstruction of cancer cell and tumor from body parts does not lead to
loss of information like 2D shape visualization. Various research methodologies for segmentation and detection
for 3D reconstruction of cancer cell and tumor were performed by previous research. However, the pursuit for
better methodology for segmentation and detection for 3D reconstruction of cancer cell and tumor are still
unsolved research problem due to lack of efficient feature extraction for details surface information,
misclassification during training phases and low tissue contrast which causes low detection and precision rate
with high computational complexity during detection and segmentation. This research addresses comprehensive
and critical review of various segmentation and detection research methodologies for cancer affected cell and
tumor in human body in the basis of three-dimensional reconstruction from MRI or CT images. At first, core
research background is illustrated highlighting various aspects addressed by this research. After that, various
previous methods with advantages and disadvantages followed by various phases used as frameworks exist in
the previous research demonstrated by this research. Then, extensive experimental evaluations done by previous
research are demonstrated by this research with various performance metrics. At last, this research summarized
overall observation on previous research categorized into two aspects, i.e. observation on common research
methodologies and recommended area for further research. Overall reviews proposed in this paper have been
extensively studied in various research papers which can significantly contribute to computer vision research
and can be potential for future development and direction for future research
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