98 research outputs found
Machine Learning/Deep Learning in Medical Image Processing
Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue
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
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
COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images
OBJECTIVE: Chest CT can display the main pathogenic factors of chronic obstructive pulmonary disease (COPD), emphysema and airway wall remodeling. This study aims to establish deep convolutional neural network (CNN) models using these two imaging markers to diagnose and grade COPD. METHODS: Subjects who underwent chest CT and pulmonary function test (PFT) from one hospital (n = 373) were retrospectively included as the training cohort, and subjects from another hospital (n = 226) were used as the external test cohort. According to the PFT results, all subjects were labeled as Global Initiative for Chronic Obstructive Lung Disease (GOLD) Grade 1, 2, 3, 4 or normal. Two DenseNet-201 CNNs were trained using CT images of lung parenchyma and bronchial wall to generate two corresponding confidence levels to indicate the possibility of COPD, then combined with logistic regression analysis. Quantitative CT was used for comparison. RESULTS: In the test cohort, CNN achieved an area under the curve of 0.899 (95%CI: 0.853-0.935) to determine the existence of COPD, and an accuracy of 81.7% (76.2-86.7%), which was significantly higher than the accuracy 68.1% (61.6%-74.2%) using quantitative CT method (p < 0.05). For three-way (normal, GOLD 1-2, and GOLD 3-4) and five-way (normal, GOLD 1, 2, 3, and 4) classifications, CNN reached accuracies of 77.4 and 67.9%, respectively. CONCLUSION: CNN can identify emphysema and airway wall remodeling on CT images to infer lung function and determine the existence and severity of COPD. It provides an alternative way to detect COPD using the extensively available chest CT. ADVANCES IN KNOWLEDGE: CNN can identify the main pathological changes of COPD (emphysema and airway wall remodeling) based on CT images, to infer lung function and determine the existence and severity of COPD. CNN reached an area under the curve of 0.853 to determine the existence of COPD in the external test cohort. The CNN approach provides an alternative and effective way for early detection of COPD using extensively used chest CT, as an important alternative to pulmonary function test
Spatiotemporal Modeling Encounters 3D Medical Image Analysis: Slice-Shift UNet with Multi-View Fusion
As a fundamental part of computational healthcare, Computer Tomography (CT)
and Magnetic Resonance Imaging (MRI) provide volumetric data, making the
development of algorithms for 3D image analysis a necessity. Despite being
computationally cheap, 2D Convolutional Neural Networks can only extract
spatial information. In contrast, 3D CNNs can extract three-dimensional
features, but they have higher computational costs and latency, which is a
limitation for clinical practice that requires fast and efficient models.
Inspired by the field of video action recognition we propose a new 2D-based
model dubbed Slice SHift UNet (SSH-UNet) which encodes three-dimensional
features at 2D CNN's complexity. More precisely multi-view features are
collaboratively learned by performing 2D convolutions along the three
orthogonal planes of a volume and imposing a weights-sharing mechanism. The
third dimension, which is neglected by the 2D convolution, is reincorporated by
shifting a portion of the feature maps along the slices' axis. The
effectiveness of our approach is validated in Multi-Modality Abdominal
Multi-Organ Segmentation (AMOS) and Multi-Atlas Labeling Beyond the Cranial
Vault (BTCV) datasets, showing that SSH-UNet is more efficient while on par in
performance with state-of-the-art architectures
AeroPath: An airway segmentation benchmark dataset with challenging pathology
To improve the prognosis of patients suffering from pulmonary diseases, such
as lung cancer, early diagnosis and treatment are crucial. The analysis of CT
images is invaluable for diagnosis, whereas high quality segmentation of the
airway tree are required for intervention planning and live guidance during
bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22)
challenge released a large dataset, both enabling training of deep-learning
based models and bringing substantial improvement of the state-of-the-art for
the airway segmentation task. However, the ATM'22 dataset includes few patients
with severe pathologies affecting the airway tree anatomy. In this study, we
introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images
from patients with pathologies ranging from emphysema to large tumors, with
corresponding trachea and bronchi annotations. Second, we present a multiscale
fusion design for automatic airway segmentation. Models were trained on the
ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against
competitive open-source methods. The same performance metrics as used in the
ATM'22 challenge were used to benchmark the different considered approaches.
Lastly, an open web application is developed, to easily test the proposed model
on new data. The results demonstrated that our proposed architecture predicted
topologically correct segmentations for all the patients included in the
AeroPath dataset. The proposed method is robust and able to handle various
anomalies, down to at least the fifth airway generation. In addition, the
AeroPath dataset, featuring patients with challenging pathologies, will
contribute to development of new state-of-the-art methods. The AeroPath dataset
and the web application are made openly available.Comment: 13 pages, 5 figures, submitted to Scientific Report
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