1,108 research outputs found
Multi-scale analysis of lung computed tomography images
A computer-aided detection (CAD) system for the identification of lung
internal nodules in low-dose multi-detector helical Computed Tomography (CT)
images was developed in the framework of the MAGIC-5 project. The three modules
of our lung CAD system, a segmentation algorithm for lung internal region
identification, a multi-scale dot-enhancement filter for nodule candidate
selection and a multi-scale neural technique for false positive finding
reduction, are described. The results obtained on a dataset of low-dose and
thin-slice CT scans are shown in terms of free response receiver operating
characteristic (FROC) curves and discussed.Comment: 18 pages, 12 low-resolution figure
Computer-aided detection of pulmonary nodules in low-dose CT
A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector helical CT images with 1.25 mm slice
thickness is being developed in the framework of the INFN-supported MAGIC-5
Italian project. The basic modules of our lung-CAD system, a dot enhancement
filter for nodule candidate selection and a voxel-based neural classifier for
false-positive finding reduction, are described. Preliminary results obtained
on the so-far collected database of lung CT scans are discussed.Comment: 3 pages, 4 figures; Proceedings of the CompIMAGE - International
Symposium on Computational Modelling of Objects Represented in Images:
Fundamentals, Methods and Applications, 20-21 Oct. 2006, Coimbra, Portuga
Deep convolutional neural networks for multi-planar lung nodule detection:improvement in small nodule identification
Objective: In clinical practice, small lung nodules can be easily overlooked by radiologists. The paper aims to provide an efficient and accurate detection system for small lung nodules while keeping good performance for large nodules. Methods: We propose a multi-planar detection system using convolutional neural networks. The 2-D convolutional neural network model, U-net++, was trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are combined. For false positive reduction, we apply 3-D multi-scale dense convolutional neural networks to efficiently remove false positive candidates. We use the public LIDC-IDRI dataset which includes 888 CT scans with 1186 nodules annotated by four radiologists. Results: After ten-fold cross-validation, our proposed system achieves a sensitivity of 94.2% with 1.0 false positive/scan and a sensitivity of 96.0% with 2.0 false positives/scan. Although it is difficult to detect small nodules (i.e. < 6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall false positive rate of 1.0 (2.0) false positives/scan. At the nodule candidate detection stage, results show that a multi-planar method is capable to detect more nodules compared to using a single plane. Conclusion: Our approach achieves good performance not only for small nodules, but also for large lesions on this dataset. This demonstrates the effectiveness and efficiency of our developed CAD system for lung nodule detection. Significance: The proposed system could provide support for radiologists on early detection of lung cancer
Deep convolutional neural networks for multi-planar lung nodule detection: improvement in small nodule identification
Objective: In clinical practice, small lung nodules can be easily overlooked
by radiologists. The paper aims to provide an efficient and accurate detection
system for small lung nodules while keeping good performance for large nodules.
Methods: We propose a multi-planar detection system using convolutional neural
networks. The 2-D convolutional neural network model, U-net++, was trained by
axial, coronal, and sagittal slices for the candidate detection task. All
possible nodule candidates from the three different planes are combined. For
false positive reduction, we apply 3-D multi-scale dense convolutional neural
networks to efficiently remove false positive candidates. We use the public
LIDC-IDRI dataset which includes 888 CT scans with 1186 nodules annotated by
four radiologists. Results: After ten-fold cross-validation, our proposed
system achieves a sensitivity of 94.2% with 1.0 false positive/scan and a
sensitivity of 96.0% with 2.0 false positives/scan. Although it is difficult to
detect small nodules (i.e. < 6 mm), our designed CAD system reaches a
sensitivity of 93.4% (95.0%) of these small nodules at an overall false
positive rate of 1.0 (2.0) false positives/scan. At the nodule candidate
detection stage, results show that a multi-planar method is capable to detect
more nodules compared to using a single plane. Conclusion: Our approach
achieves good performance not only for small nodules, but also for large
lesions on this dataset. This demonstrates the effectiveness and efficiency of
our developed CAD system for lung nodule detection. Significance: The proposed
system could provide support for radiologists on early detection of lung
cancer
Lung Nodule Detection in Screening Computed Tomography
A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector helical Computed Tomography (CT) images with
1.25 mm slice thickness is presented. The basic modules of our lung-CAD system,
a dot-enhancement filter for nodule candidate selection and a neural classifier
for false-positive finding reduction, are described. The results obtained on
the collected database of lung CT scans are discussed.Comment: 3 pages, 4 figures; Proceedings of the IEEE NNS and MIC Conference,
Oct. 29 - Nov. 4, 2006, San Diego, Californi
Semi-supervised multi-task learning for lung cancer diagnosis
Early detection of lung nodules is of great importance in lung cancer
screening. Existing research recognizes the critical role played by CAD systems
in early detection and diagnosis of lung nodules. However, many CAD systems,
which are used as cancer detection tools, produce a lot of false positives (FP)
and require a further FP reduction step. Furthermore, guidelines for early
diagnosis and treatment of lung cancer are consist of different shape and
volume measurements of abnormalities. Segmentation is at the heart of our
understanding of nodules morphology making it a major area of interest within
the field of computer aided diagnosis systems. This study set out to test the
hypothesis that joint learning of false positive (FP) nodule reduction and
nodule segmentation can improve the computer aided diagnosis (CAD) systems'
performance on both tasks. To support this hypothesis we propose a 3D deep
multi-task CNN to tackle these two problems jointly. We tested our system on
LUNA16 dataset and achieved an average dice similarity coefficient (DSC) of 91%
as segmentation accuracy and a score of nearly 92% for FP reduction. As a proof
of our hypothesis, we showed improvements of segmentation and FP reduction
tasks over two baselines. Our results support that joint training of these two
tasks through a multi-task learning approach improves system performance on
both. We also showed that a semi-supervised approach can be used to overcome
the limitation of lack of labeled data for the 3D segmentation task.Comment: Accepted for publication at IEEE EMBC (40th International Engineering
in Medicine and Biology Conference
An automated system for lung nodule detection in low-dose computed tomography
A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector helical Computed Tomography (CT) images was
developed in the framework of the MAGIC-5 Italian project. One of the main
goals of this project is to build a distributed database of lung CT scans in
order to enable automated image analysis through a data and cpu GRID
infrastructure. The basic modules of our lung-CAD system, a dot-enhancement
filter for nodule candidate selection and a neural classifier for
false-positive finding reduction, are described. The system was designed and
tested for both internal and sub-pleural nodules. The results obtained on the
collected database of low-dose thin-slice CT scans are shown in terms of free
response receiver operating characteristic (FROC) curves and discussed.Comment: 9 pages, 9 figures; Proceedings of the SPIE Medical Imaging
Conference, 17-22 February 2007, San Diego, California, USA, Vol. 6514,
65143
Automated detection of lung nodules in low-dose computed tomography
A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector computed-tomography (CT) images has been
developed in the framework of the MAGIC-5 Italian project. One of the main
goals of this project is to build a distributed database of lung CT scans in
order to enable automated image analysis through a data and cpu GRID
infrastructure. The basic modules of our lung-CAD system, consisting in a 3D
dot-enhancement filter for nodule detection and a neural classifier for
false-positive finding reduction, are described. The system was designed and
tested for both internal and sub-pleural nodules. The database used in this
study consists of 17 low-dose CT scans reconstructed with thin slice thickness
(~300 slices/scan). The preliminary results are shown in terms of the FROC
analysis reporting a good sensitivity (85% range) for both internal and
sub-pleural nodules at an acceptable level of false positive findings (1-9
FP/scan); the sensitivity value remains very high (75% range) even at 1-6
FP/scanComment: 4 pages, 2 figures: Proceedings of the Computer Assisted Radiology
and Surgery, 21th International Congress and Exhibition, Berlin, Volume 2,
Supplement 1, June 2007, pp 357-35
Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection
Accurate pulmonary nodule detection is a crucial step in lung cancer
screening. Computer-aided detection (CAD) systems are not routinely used by
radiologists for pulmonary nodule detection in clinical practice despite their
potential benefits. Maximum intensity projection (MIP) images improve the
detection of pulmonary nodules in radiological evaluation with computed
tomography (CT) scans. Inspired by the clinical methodology of radiologists, we
aim to explore the feasibility of applying MIP images to improve the
effectiveness of automatic lung nodule detection using convolutional neural
networks (CNNs). We propose a CNN-based approach that takes MIP images of
different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices
as input. Such an approach augments the two-dimensional (2-D) CT slice images
with more representative spatial information that helps discriminate nodules
from vessels through their morphologies. Our proposed method achieves
sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19%
with 2 false positives per scan for lung nodule detection on 888 scans in the
LIDC-IDRI dataset. The use of thick MIP images helps the detection of small
pulmonary nodules (3 mm-10 mm) and results in fewer false positives.
Experimental results show that utilizing MIP images can increase the
sensitivity and lower the number of false positives, which demonstrates the
effectiveness and significance of the proposed MIP-based CNNs framework for
automatic pulmonary nodule detection in CT scans. The proposed method also
shows the potential that CNNs could gain benefits for nodule detection by
combining the clinical procedure.Comment: Submitted to IEEE TM
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