366 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
Tubular Structure Segmentation Using Spatial Fully Connected Network with Radial Distance Loss for 3D Medical Images
This paper presents a new spatial fully connected tubular network for 3D tubular-structure segmentation. Automatic and complete segmentation of intricate tubular structures remains an unsolved challenge in the medical image analysis. Airways and vasculature pose high demands on medical image analysis as they are elongated fine structures with calibers ranging from several tens of voxels to voxel-level resolution, branching in deeply multi-scale fashion, and with complex topological and spatial relationships. Most machine/deep learning approaches are based on intensity features and ignore spatial consistency across the network that are otherwise distinct in tubular structures. In this work, we introduce 3D slice-by-slice convolutional layers in a U-Net architecture to capture the spatial information of elongated structures. Furthermore, we present a novel loss function, coined radial distance loss, specifically designed for tubular structures. The commonly used methods of cross-entropy loss and generalized Dice loss are sensitive to volumetric variation. However, in tiny tubular structure segmentation, topological errors are as important as volumetric errors. The proposed radial distance loss places higher weight to the centerline, and this weight decreases along the radial direction. Radial distance loss can help networks focus more attention on tiny structures than on thicker tubular structures. We perform experiments on bronchus segmentation on 3D CT images. The experimental results show that compared to the baseline U-Net, our proposed network achieved improvement about 24% and 30% in Dice index and centerline over ratio
An Entire Renal Anatomy Extraction Network for Advanced CAD During Partial Nephrectomy
Partial nephrectomy (PN) is common surgery in urology. Digitization of renal
anatomies brings much help to many computer-aided diagnosis (CAD) techniques
during PN. However, the manual delineation of kidney vascular system and tumor
on each slice is time consuming, error-prone, and inconsistent. Therefore, we
proposed an entire renal anatomies extraction method from Computed Tomographic
Angiographic (CTA) images fully based on deep learning. We adopted a
coarse-to-fine workflow to extract target tissues: first, we roughly located
the kidney region, and then cropped the kidney region for more detail
extraction. The network we used in our workflow is based on 3D U-Net. To
dealing with the imbalance of class contributions to loss, we combined the dice
loss with focal loss, and added an extra weight to prevent excessive attention.
We also improved the manual annotations of vessels by merging semi-trained
model's prediction and original annotations under supervision. We performed
several experiments to find the best-fitting combination of variables for
training. We trained and evaluated the models on our 60 cases dataset with 3
different sources. The average dice score coefficient (DSC) of kidney, tumor,
cyst, artery, and vein, were 90.9%, 90.0%, 89.2%, 80.1% and 82.2% respectively.
Our modulate weight and hybrid strategy of loss function increased the average
DSC of all tissues about 8-20%. Our optimization of vessel annotation improved
the average DSC about 1-5%. We proved the efficiency of our network on renal
anatomies segmentation. The high accuracy and fully automation make it possible
to quickly digitize the personal renal anatomies, which greatly increases the
feasibility and practicability of CAD application on urology surgery
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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
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
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
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