89 research outputs found
Deep learning for image-based liver analysis — 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
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor
Segmentation Benchmark (LITS) organized in conjunction with the IEEE
International Symposium on Biomedical Imaging (ISBI) 2016 and International
Conference On Medical Image Computing Computer Assisted Intervention (MICCAI)
2017. Twenty four valid state-of-the-art liver and liver tumor segmentation
algorithms were applied to a set of 131 computed tomography (CT) volumes with
different types of tumor contrast levels (hyper-/hypo-intense), abnormalities
in tissues (metastasectomie) size and varying amount of lesions. The submitted
algorithms have been tested on 70 undisclosed volumes. The dataset is created
in collaboration with seven hospitals and research institutions and manually
reviewed by independent three radiologists. We found that not a single
algorithm performed best for liver and tumors. The best liver segmentation
algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation
the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image
data and manual annotations continue to be publicly available through an online
evaluation system as an ongoing benchmarking resource.Comment: conferenc
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
Human treelike tubular structure segmentation: A comprehensive review and future perspectives
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives
Various structures in human physiology follow a treelike morphology, which
often expresses complexity at very fine scales. Examples of such structures are
intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large
collections of 2D and 3D images have been made available by medical imaging
modalities such as magnetic resonance imaging (MRI), computed tomography (CT),
Optical coherence tomography (OCT) and ultrasound in which the spatial
arrangement can be observed. Segmentation of these structures in medical
imaging is of great importance since the analysis of the structure provides
insights into disease diagnosis, treatment planning, and prognosis. Manually
labelling extensive data by radiologists is often time-consuming and
error-prone. As a result, automated or semi-automated computational models have
become a popular research field of medical imaging in the past two decades, and
many have been developed to date. In this survey, we aim to provide a
comprehensive review of currently publicly available datasets, segmentation
algorithms, and evaluation metrics. In addition, current challenges and future
research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa
U-net and its variants for medical image segmentation: A review of theory and applications
U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net
Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging
Automated blood vessel segmentation is vital for biomedical imaging, as
vessel changes indicate many pathologies. Still, precise segmentation is
difficult due to the complexity of vascular structures, anatomical variations
across patients, the scarcity of annotated public datasets, and the quality of
images. We present a thorough literature review, highlighting the state of
machine learning techniques across diverse organs. Our goal is to provide a
foundation on the topic and identify a robust baseline model for application to
vascular segmentation in a new imaging modality, Hierarchical Phase Contrast
Tomography (HiP CT). Introduced in 2020 at the European Synchrotron Radiation
Facility, HiP CT enables 3D imaging of complete organs at an unprecedented
resolution of ca. 20mm per voxel, with the capability for localized zooms in
selected regions down to 1mm per voxel without sectioning. We have created a
training dataset with double annotator validated vascular data from three
kidneys imaged with HiP CT in the context of the Human Organ Atlas Project.
Finally, utilising the nnU Net model, we conduct experiments to assess the
models performance on both familiar and unseen samples, employing vessel
specific metrics. Our results show that while segmentations yielded reasonably
high scores such as clDice values ranging from 0.82 to 0.88, certain errors
persisted. Large vessels that collapsed due to the lack of hydrostatic pressure
(HiP CT is an ex vivo technique) were segmented poorly. Moreover, decreased
connectivity in finer vessels and higher segmentation errors at vessel
boundaries were observed. Such errors obstruct the understanding of the
structures by interrupting vascular tree connectivity. Through our review and
outputs, we aim to set a benchmark for subsequent model evaluations using
various modalities, especially with the HiP CT imaging database
Medical Image Segmentation Review: The success of U-Net
Automatic medical image segmentation is a crucial topic in the medical domain
and successively a critical counterpart in the computer-aided diagnosis
paradigm. U-Net is the most widespread image segmentation architecture due to
its flexibility, optimized modular design, and success in all medical image
modalities. Over the years, the U-Net model achieved tremendous attention from
academic and industrial researchers. Several extensions of this network have
been proposed to address the scale and complexity created by medical tasks.
Addressing the deficiency of the naive U-Net model is the foremost step for
vendors to utilize the proper U-Net variant model for their business. Having a
compendium of different variants in one place makes it easier for builders to
identify the relevant research. Also, for ML researchers it will help them
understand the challenges of the biological tasks that challenge the model. To
address this, we discuss the practical aspects of the U-Net model and suggest a
taxonomy to categorize each network variant. Moreover, to measure the
performance of these strategies in a clinical application, we propose fair
evaluations of some unique and famous designs on well-known datasets. We
provide a comprehensive implementation library with trained models for future
research. In addition, for ease of future studies, we created an online list of
U-Net papers with their possible official implementation. All information is
gathered in https://github.com/NITR098/Awesome-U-Net repository.Comment: Submitted to the IEEE Transactions on Pattern Analysis and Machine
Intelligence Journa
U-Net and its variants for medical image segmentation: theory and applications
U-net is an image segmentation technique developed primarily for medical
image analysis that can precisely segment images using a scarce amount of
training data. These traits provide U-net with a very high utility within the
medical imaging community and have resulted in extensive adoption of U-net as
the primary tool for segmentation tasks in medical imaging. The success of
U-net is evident in its widespread use in all major image modalities from CT
scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a
segmentation tool, there have been instances of the use of U-net in other
applications. As the potential of U-net is still increasing, in this review we
look at the various developments that have been made in the U-net architecture
and provide observations on recent trends. We examine the various innovations
that have been made in deep learning and discuss how these tools facilitate
U-net. Furthermore, we look at image modalities and application areas where
U-net has been applied.Comment: 42 pages, in IEEE Acces
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