95 research outputs found
Deep learning for image-based liver analysis β A comprehensive review focusing on malignant lesions
Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio
Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations
There is no denying how machine learning and computer vision have grown in
the recent years. Their highest advantages lie within their automation,
suitability, and ability to generate astounding results in a matter of seconds
in a reproducible manner. This is aided by the ubiquitous advancements reached
in the computing capabilities of current graphical processing units and the
highly efficient implementation of such techniques. Hence, in this paper, we
survey the key studies that are published between 2014 and 2020, showcasing the
different machine learning algorithms researchers have used to segment the
liver, hepatic-tumors, and hepatic-vasculature structures. We divide the
surveyed studies based on the tissue of interest (hepatic-parenchyma,
hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more
than one task simultaneously. Additionally, the machine learning algorithms are
classified as either supervised or unsupervised, and further partitioned if the
amount of works that fall under a certain scheme is significant. Moreover,
different datasets and challenges found in literature and websites, containing
masks of the aforementioned tissues, are thoroughly discussed, highlighting the
organizers original contributions, and those of other researchers. Also, the
metrics that are used excessively in literature are mentioned in our review
stressing their relevancy to the task at hand. Finally, critical challenges and
future directions are emphasized for innovative researchers to tackle, exposing
gaps that need addressing such as the scarcity of many studies on the vessels
segmentation challenge, and why their absence needs to be dealt with in an
accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver
tissues segmentation based on automated ML-based technique
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : μκ³Όλν μκ³Όνκ³Ό, 2020. 8. μ΄μ¬μ±.Over the past decade, the application of magnetic resonance imaging (MRI) in the field of diagnosis and treatment has increased. MRI provides higher soft-tissue contrast, especially in the brain, abdominal organ, and bone marrow without the expose of ionizing radiation. Hence, simultaneous positron emission tomography/MR (PET/MR) system and MR-image guided radiation therapy (MR-IGRT) system has recently been emerged and currently available for clinical study.
One major issue in PET/MR system is attenuation correction from MRI scans for PET quantification and a similar need for the assignment of electron densities to MRI scans for dose calculation can be found in MR-IGRT system. Because the MR signals are related to the proton density and relaxation properties of tissue, not to electron density. To overcome this problem, the method called synthetic CT (sCT), a pseudo CT derived from MR images, has been proposed. In this thesis, studies on generating synthetic CT and investigating the feasibility of using a MR-based synthetic CT for diagnostic and radiotherapy application were presented.
Firstly, MR image-based attenuation correction (MR-AC) method using level-set segmentation for brain PET/MRI was developed. To resolve conventional inaccuracy MR-AC problem, we proposed an improved ultrashort echo time MR-AC method that was based on a multiphase level-set algorithm with main magnetic field inhomogeneity correction. We also assessed the feasibility of level-set based MR-AC method, compared with CT-AC and MR-AC provided by the manufacturer of the PET/MRI scanner.
Secondly, we proposed sCT generation from the low field MR images using 2D convolution neural network model for MR-IGRT system. This sCT images were compared to the deformed CT generated using the deformable registration being used in the current system. We assessed the feasibility of using sCT for radiation treatment planning from each of the patients with pelvic, thoraic and abdominal region through geometric and dosimetric evaluation.μ§λ 10λ
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μ κ²½λ§ λͺ¨λΈμ μ¬μ©νμ¬ μ νλ MR μ΄λ―Έμ§μμ μμ±λ ν©μ± CT λ°©λ²λ₯Ό μ μνμλ€. μ΄ ν©μ± CT μ΄λ―Έμ§λ₯Ό λ³ν μ ν©μ μ¬μ©νμ¬ μμ±λ λ³ν CTμ λΉκ΅ νμλ€. λν 골λ°, νλΆ λ° λ³΅λΆ νμμμμ κΈ°ννμ , μ λμ λΆμμ ν΅ν΄ λ°©μ¬μ μΉλ£κ³νμμμ ν©μ± CTλ₯Ό μ¬μ©κ°λ₯μ±μ νκ°νμλ€.Chapter 1. Introduction 1
1.1. Background 1
1.1.1. The Integration of MRI into Other Medical Devices 1
1.1.2. Chanllenges in the MRI Integrated System 4
1.1.3. Synthetic CT Generation 5
1.2. Purpose of Research 6
Chapter 2. MRI-based Attenuation Correction for PET/MRI 8
2.1. Background 8
2.2. Materials and Methods 10
2.2.1. Brain PET Dataset 19
2.2.2. MR-Based Attenuation Map using Level-Set Algorithm 12
2.2.3. Image Processing and Reconstruction 18
2.3. Results 20
2.4. Discussion 28
Chapter 3. MRI-based synthetic CT generation for MR-IGRT 30
3.1. Background 30
3.2. Materials and Methods 32
3.2.1. MR-dCT Paired DataSet 32
3.2.2. Synthetic CT Generation using 2D CNN 36
3.2.3. Data Analysis 38
3.3. Results 41
3.3.1. Image Comparison 41
3.3.2. Geometric Analysis 49
3.3.3. Dosimetric Analysis 49
3.4. Discussion 56
Chapter 4. Conclusions 59
Bibliography 60
Abstract in Korean (κ΅λ¬Έ μ΄λ‘) 64Docto
Impact of Image Denoising Techniques on CNN-based Liver Vessel Segmentation using Synthesis Low-dose Contrast Enhanced CT Images
Liver vessel segmentation in contrast enhanced CT (CECT) image is relevant for several clinical applications. However, the liver segmentation on noisy images obtain incorrect liver vessel segmentation which may lead to distortion in the simulation of cooling effect near the vessels during the planning. In this study, we present a framework that consists of three well-known and state-of-the-art denoising techniques, Vesssel enhancing diffusion (VED), RED-CNN, and MAP-NN and using a state-of-the-art Convolution Neural Networks (nn-Unet) to segment the liver vessels from the CECT images. The impact of denoising methods on the vessel segmentation are ablated using with multi-level simulated low-dose CECT of the liver. The experiment is carried on CECT images of the liver from two public and one private datasets. We evaluate the performance of the framework using Dice score and sensitivity criteria. Furthermore, we investigate the efficient of denoising on roughness of the surface of liver vessel segmentation. The results from our experiment suggest that denoising methods can improve the liver vessel segmentation quality in the CECT image with high low-dose noise while they degrade the liver vessel segmentation accuracy for low-noise-level CECT images
Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations
Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results. Hence, in this paper, we survey the key studies that are published between 2014 and 2022, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites containing masks of the aforementioned tissues are thoroughly discussed, highlighting the organizers' original contributions and those of other researchers. Also, the metrics used excessively in the literature are mentioned in our review, stressing their relevance to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing, such as the scarcity of many studies on the vessels' segmentation challenge and why their absence needs to be dealt with sooner than later. 2022 The Author(s)This publication was made possible by an Award [GSRA6-2-0521-19034] from Qatar National Research Fund (a member of Qatar Foundation). The contents herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National LibraryScopu
Deep Semantic Segmentation of Natural and Medical Images: A Review
The semantic image segmentation task consists of classifying each pixel of an
image into an instance, where each instance corresponds to a class. This task
is a part of the concept of scene understanding or better explaining the global
context of an image. In the medical image analysis domain, image segmentation
can be used for image-guided interventions, radiotherapy, or improved
radiological diagnostics. In this review, we categorize the leading deep
learning-based medical and non-medical image segmentation solutions into six
main groups of deep architectural, data synthesis-based, loss function-based,
sequenced models, weakly supervised, and multi-task methods and provide a
comprehensive review of the contributions in each of these groups. Further, for
each group, we analyze each variant of these groups and discuss the limitations
of the current approaches and present potential future research directions for
semantic image segmentation.Comment: 45 pages, 16 figures. Accepted for publication in Springer Artificial
Intelligence Revie
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