61 research outputs found
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
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
S4ND: Single-Shot Single-Scale Lung Nodule Detection
The state of the art lung nodule detection studies rely on computationally
expensive multi-stage frameworks to detect nodules from CT scans. To address
this computational challenge and provide better performance, in this paper we
propose S4ND, a new deep learning based method for lung nodule detection. Our
approach uses a single feed forward pass of a single network for detection and
provides better performance when compared to the current literature. The whole
detection pipeline is designed as a single Convolutional Neural Network
(CNN) with dense connections, trained in an end-to-end manner. S4ND does not
require any further post-processing or user guidance to refine detection
results. Experimentally, we compared our network with the current
state-of-the-art object detection network (SSD) in computer vision as well as
the state-of-the-art published method for lung nodule detection (3D DCNN). We
used publically available CT scans from LUNA challenge dataset and showed
that the proposed method outperforms the current literature both in terms of
efficiency and accuracy by achieving an average FROC-score of . We also
provide an in-depth analysis of our proposed network to shed light on the
unclear paradigms of tiny object detection.Comment: Accepted for publication at MICCAI 2018 (21st International
Conference on Medical Image Computing and Computer Assisted Intervention
Deep learning for lung cancer on computed tomography:early detection and prognostic prediction
Lung cancer is one of the most fatal cancers in the world, the leading cause of death among both men and women. The five-year survival rate for lung cancer patients is only between 10 and 20%. However, the mortality rate can be reduced if lung cancer is diagnosed at an early stage and treated promptly. Screening trials have been established in many countries to improve early detetion of lung cancer, but it results in numerous scans that need to be evaluated, which is labor-intensive. On the other hand, when lung cancer is diagnosed at an early stage in screening, the clinical response after the treatment can vary between patients. Therefore, strong needs exist for accurate early detection and prognostic prediction of lung cancer.Deep learning recently has achieved great success in medical image analysis, especially for lung cancer. The results described in this thesis show that combining clinical procedures, deep learning techniques are feasible to assist radiologists with pulmonary nodule detection and rule out most negative scans in lung cancer screening. Besides, by integrating clinical factors and imaging features, deep learning can identify high mortality risk lung cancer patients who could benefit from adjuvant chemotherapy. With the implementation of lung cancer screening programs, more imaging and clinical data will be available, which enables deep learning to further boost the efficiency of screening procedures and lower the lung cancer mortality in the future
LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection
Convolutional neural networks (CNNs) have been demonstrated to be highly
effective in the field of pulmonary nodule detection. However, existing CNN
based pulmonary nodule detection methods lack the ability to capture long-range
dependencies, which is vital for global information extraction. In computer
vision tasks, non-local operations have been widely utilized, but the
computational cost could be very high for 3D computed tomography (CT) images.
To address this issue, we propose a long short slice-aware network (LSSANet)
for the detection of pulmonary nodules. In particular, we develop a new
non-local mechanism termed long short slice grouping (LSSG), which splits the
compact non-local embeddings into a short-distance slice grouped one and a
long-distance slice grouped counterpart. This not only reduces the
computational burden, but also keeps long-range dependencies among any elements
across slices and in the whole feature map. The proposed LSSG is easy-to-use
and can be plugged into many pulmonary nodule detection networks. To verify the
performance of LSSANet, we compare with several recently proposed and
competitive detection approaches based on 2D/3D CNN. Promising evaluation
results on the large-scale PN9 dataset demonstrate the effectiveness of our
method. Code is at https://github.com/Ruixxxx/LSSANet.Comment: MICCAI 202
Segmentation and classification of lung nodules from Thoracic CT scans : methods based on dictionary learning and deep convolutional neural networks.
Lung cancer is a leading cause of cancer death in the world. Key to survival of patients is early diagnosis. Studies have demonstrated that screening high risk patients with Low-dose Computed Tomography (CT) is invaluable for reducing morbidity and mortality. Computer Aided Diagnosis (CADx) systems can assist radiologists and care providers in reading and analyzing lung CT images to segment, classify, and keep track of nodules for signs of cancer. In this thesis, we propose a CADx system for this purpose. To predict lung nodule malignancy, we propose a new deep learning framework that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to learn best in-plane and inter-slice visual features for diagnostic nodule classification. Since a nodule\u27s volumetric growth and shape variation over a period of time may reveal information regarding the malignancy of nodule, separately, a dictionary learning based approach is proposed to segment the nodule\u27s shape at two time points from two scans, one year apart. The output of a CNN classifier trained to learn visual appearance of malignant nodules is then combined with the derived measures of shape change and volumetric growth in assigning a probability of malignancy to the nodule. Due to the limited number of available CT scans of benign and malignant nodules in the image database from the National Lung Screening Trial (NLST), we chose to initially train a deep neural network on the larger LUNA16 Challenge database which was built for the purpose of eliminating false positives from detected nodules in thoracic CT scans. Discriminative features that were learned in this application were transferred to predict malignancy. The algorithm for segmenting nodule shapes in serial CT scans utilizes a sparse combination of training shapes (SCoTS). This algorithm captures a sparse representation of a shape in input data through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During evolution, the shape prior influence is adjusted based on shape reconstruction, with the assigned weight determined from the degree of sparsity of the representation. The discriminative nature of sparse representation, affords us the opportunity to compare nodules\u27 variations in consecutive time points and to predict malignancy. Experimental validations of the proposed segmentation algorithm have been demonstrated on 542 3-D lung nodule data from the LIDC-IDRI database which includes radiologist delineated nodule boundaries. The effectiveness of the proposed deep learning and dictionary learning architectures for malignancy prediction have been demonstrated on CT data from 370 biopsied subjects collected from the NLST database. Each subject in this database had at least two serial CT scans at two separate time points one year apart. The proposed RNN CAD system achieved an ROC Area Under the Curve (AUC) of 0.87, when validated on CT data from nodules at second sequential time point and 0.83 based on dictionary learning method; however, when nodule shape change and appearance were combined, the classifier performance improved to AUC=0.89
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