249 research outputs found
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
The medical image analysis field has traditionally been focused on the
development of organ-, and disease-specific methods. Recently, the interest in
the development of more 20 comprehensive computational anatomical models has
grown, leading to the creation of multi-organ models. Multi-organ approaches,
unlike traditional organ-specific strategies, incorporate inter-organ relations
into the model, thus leading to a more accurate representation of the complex
human anatomy. Inter-organ relations are not only spatial, but also functional
and physiological. Over the years, the strategies 25 proposed to efficiently
model multi-organ structures have evolved from the simple global modeling, to
more sophisticated approaches such as sequential, hierarchical, or machine
learning-based models. In this paper, we present a review of the state of the
art on multi-organ analysis and associated computation anatomy methodology. The
manuscript follows a methodology-based classification of the different
techniques 30 available for the analysis of multi-organs and multi-anatomical
structures, from techniques using point distribution models to the most recent
deep learning-based approaches. With more than 300 papers included in this
review, we reflect on the trends and challenges of the field of computational
anatomy, the particularities of each anatomical region, and the potential of
multi-organ analysis to increase the impact of 35 medical imaging applications
on the future of healthcare.Comment: Paper under revie
AUTOMATIC LIVER SEGMENTATION FROM CT SCANS USING INTENSITY ANALYSIS AND LEVEL-SET ACTIVE CONTOURS
Liver segmentation from CT scans is still a challenging task due to the liver characteristics in terms of shape and intensity variability. In this work, we propose an automatic segmentation method of the liver from CT data sets. The framework consists of three main steps: liver shape model localization, liver intensity range estimation and localized active contouring. We proposed an adaptive multiple thresholding technique to estimate the range of the liver intensities. First, multiple thresholding is used to extract the dense tissue from the whole CT scan. A localization step is then used to find the approximate location of the liver in the CT scan, to localize a constructed mean liver shape model. A liver intensity-range estimation step is then applied within the localized shape model ROI. The localized shape model and the estimated liver intensity range are used to build the initial mask. A level set based active contour algorithm is used to deform the initial mask to the liver boundaries in the CT scan. The proposed method was evaluated on two public data sets: SLIVER07 and 3D-IRCAD. The experiments showed that the proposed method is able to segment to liver in all CT scans in the two data sets accurately
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT
scans. As the target often occupies a relatively small region in the input
image, deep neural networks can be easily confused by the complex and variable
background. To alleviate this, researchers proposed a coarse-to-fine approach,
which used prediction from the first (coarse) stage to indicate a smaller input
region for the second (fine) stage. Despite its effectiveness, this algorithm
dealt with two stages individually, which lacked optimizing a global energy
function, and limited its ability to incorporate multi-stage visual cues.
Missing contextual information led to unsatisfying convergence in iterations,
and that the fine stage sometimes produced even lower segmentation accuracy
than the coarse stage.
This paper presents a Recurrent Saliency Transformation Network. The key
innovation is a saliency transformation module, which repeatedly converts the
segmentation probability map from the previous iteration as spatial weights and
applies these weights to the current iteration. This brings us two-fold
benefits. In training, it allows joint optimization over the deep networks
dealing with different input scales. In testing, it propagates multi-stage
visual information throughout iterations to improve segmentation accuracy.
Experiments in the NIH pancreas segmentation dataset demonstrate the
state-of-the-art accuracy, which outperforms the previous best by an average of
over 2%. Much higher accuracies are also reported on several small organs in a
larger dataset collected by ourselves. In addition, our approach enjoys better
convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures
Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation
Automatic pancreas segmentation in 3D radiological scans is a critical, yet challenging task. As a prerequisite for computer-aided diagnosis (CADx) systems, accurate pancreas segmentation could generate both quantitative and qualitative information towards establishing the severity of a condition, and thus provide additional guidance for therapy planning. Since the pancreas is an organ of high inter-patient anatomical variability, previous segmentation approaches report lower quantitative accuracy scores in comparison to abdominal organs such as the liver or kidneys. This paper presents a novel approach for automatic pancreas segmentation in magnetic resonance imaging (MRI) and computer tomography (CT) scans. This method exploits 3D segmentation that, when coupled with geometrical and morphological characteristics of abdominal tissue, classifies distinct contours in tight pixel-range proximity as “pancreas” or “non-pancreas”. There are three main stages to this approach: (1) identify a major pancreas region and apply contrast enhancement to differentiate between pancreatic and surrounding tissue; (2) perform 3D segmentation via continuous max-flow and min-cuts approach, structured forest edge detection, and a training dataset of annotated pancreata; (3) eliminate non-pancreatic contours from resultant segmentation via morphological operations on area, structure and connectivity between distinct contours. The proposed method is evaluated on a dataset containing 82 CT image volumes, achieving mean Dice Similarity coefficient (DSC) of 79.3 ± 4.4%. Two MRI datasets containing 216 and 132 image volumes are evaluated, achieving mean DSC 79.6 ± 5.7% and 81.6 ± 5.1% respectively. This approach is statistically stable, reflected by lower metrics in standard deviation in comparison to state-of-the-art approaches
A New Probabilistic V-Net Model with Hierarchical Spatial Feature Transform for Efficient Abdominal Multi-Organ Segmentation
Accurate and robust abdominal multi-organ segmentation from CT imaging of
different modalities is a challenging task due to complex inter- and
intra-organ shape and appearance variations among abdominal organs. In this
paper, we propose a probabilistic multi-organ segmentation network with
hierarchical spatial-wise feature modulation to capture flexible organ semantic
variants and inject the learnt variants into different scales of feature maps
for guiding segmentation. More specifically, we design an input decomposition
module via a conditional variational auto-encoder to learn organ-specific
distributions on the low dimensional latent space and model richer organ
semantic variations that is conditioned on input images.Then by integrating
these learned variations into the V-Net decoder hierarchically via spatial
feature transformation, which has the ability to convert the variations into
conditional Affine transformation parameters for spatial-wise feature maps
modulating and guiding the fine-scale segmentation. The proposed method is
trained on the publicly available AbdomenCT-1K dataset and evaluated on two
other open datasets, i.e., 100 challenging/pathological testing patient cases
from AbdomenCT-1K fully-supervised abdominal organ segmentation benchmark and
90 cases from TCIA+&BTCV dataset. Highly competitive or superior quantitative
segmentation results have been achieved using these datasets for four abdominal
organs of liver, kidney, spleen and pancreas with reported Dice scores improved
by 7.3% for kidneys and 9.7% for pancreas, while being ~7 times faster than two
strong baseline segmentation methods(nnUNet and CoTr).Comment: 12 pages, 6 figure
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
Cloud-Based Benchmarking of Medical Image Analysis
Medical imagin
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