283 research outputs found
A Comparative Study for 2D and 3D Computer-aided Diagnosis Methods for Solitary Pulmonary Nodules
Many computer-aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to investigate the relative diagnostic accuracy of 2D and 3D methods. An additional goal is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3D methods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
We introduce CASED, a novel curriculum sampling algorithm that facilitates
the optimization of deep learning segmentation or detection models on data sets
with extreme class imbalance. We evaluate the CASED learning framework on the
task of lung nodule detection in chest CT. In contrast to two-stage solutions,
wherein nodule candidates are first proposed by a segmentation model and
refined by a second detection stage, CASED improves the training of deep nodule
segmentation models (e.g. UNet) to the point where state of the art results are
achieved using only a trivial detection stage. CASED improves the optimization
of deep segmentation models by allowing them to first learn how to distinguish
nodules from their immediate surroundings, while continuously adding a greater
proportion of difficult-to-classify global context, until uniformly sampling
from the empirical data distribution. Using CASED during training yields a
minimalist proposal to the lung nodule detection problem that tops the LUNA16
nodule detection benchmark with an average sensitivity score of 88.35%.
Furthermore, we find that models trained using CASED are robust to nodule
annotation quality by showing that comparable results can be achieved when only
a point and radius for each ground truth nodule are provided during training.
Finally, the CASED learning framework makes no assumptions with regard to
imaging modality or segmentation target and should generalize to other medical
imaging problems where class imbalance is a persistent problem.Comment: 20th International Conference on Medical Image Computing and Computer
Assisted Intervention 201
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
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
Lung cancer is the leading cause of cancer death and early diagnosis is
associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive
imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to
distinguish from vascular and bone structures using CXR. Computer vision has
previously been proposed to assist human radiologists in this task, however,
leading studies use down-sampled images and computationally expensive methods
with unproven generalization. Instead, this study localizes lung nodules using
efficient encoder-decoder neural networks that process full resolution images
to avoid any signal loss resulting from down-sampling. Encoder-decoder networks
are trained and tested using the JSRT lung nodule dataset. The networks are
used to localize lung nodules from an independent external CXR dataset.
Sensitivity and false positive rates are measured using an automated framework
to eliminate any observer subjectivity. These experiments allow for the
determination of the optimal network depth, image resolution and pre-processing
pipeline for generalized lung nodule localization. We find that nodule
localization is influenced by subtlety, with more subtle nodules being detected
in earlier training epochs. Therefore, we propose a novel self-ensemble model
from three consecutive epochs centered on the validation optimum. This ensemble
achieved a sensitivity of 85% in 10-fold internal testing with false positives
of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6
following morphological false positive reduction. This result is comparable to
more computationally complex systems based on linear and spatial filtering, but
with a sub-second inference time that is faster than other methods. The
proposed algorithm achieved excellent generalization results against an
external dataset with sensitivity of 77% at a false positive rate of 7.6
Artificial Intelligence and Interstitial Lung Disease: Diagnosis and Prognosis.
Interstitial lung disease (ILD) is now diagnosed by an ILD-board consisting of radiologists, pulmonologists, and pathologists. They discuss the combination of computed tomography (CT) images, pulmonary function tests, demographic information, and histology and then agree on one of the 200 ILD diagnoses. Recent approaches employ computer-aided diagnostic tools to improve detection of disease, monitoring, and accurate prognostication. Methods based on artificial intelligence (AI) may be used in computational medicine, especially in image-based specialties such as radiology. This review summarises and highlights the strengths and weaknesses of the latest and most significant published methods that could lead to a holistic system for ILD diagnosis. We explore current AI methods and the data use to predict the prognosis and progression of ILDs. It is then essential to highlight the data that holds the most information related to risk factors for progression, e.g., CT scans and pulmonary function tests. This review aims to identify potential gaps, highlight areas that require further research, and identify the methods that could be combined to yield more promising results in future studies
CAD system for lung nodule analysis.
Lung cancer is the deadliest type of known cancer in the United States, claiming hundreds of thousands of lives each year. However, despite the high mortality rate, the 5-year survival rate after resection of Stage 1A non–small cell lung cancer is currently in the range of 62%– 82% and in recent studies even 90%. Patient survival is highly correlated with early detection. Computed Tomography (CT) technology services the early detection of lung cancer tremendously by offering a minimally invasive medical diagnostic tool. Some early types of lung cancer begin with a small mass of tissue within the lung, less than 3 cm in diameter, called a nodule. Most nodules found in a lung are benign, but a small population of them becomes malignant over time. Expert analysis of CT scans is the first step in determining whether a nodule presents a possibility for malignancy but, due to such low spatial support, many potentially harmful nodules go undetected until other symptoms motivate a more thorough search. Computer Vision and Pattern Recognition techniques can play a significant role in aiding the process of detecting and diagnosing lung nodules. This thesis outlines the development of a CAD system which, given an input CT scan, provides a functional and fast, second-opinion diagnosis to physicians. The entire process of lung nodule screening has been cast as a system, which can be enhanced by modern computing technology, with the hopes of providing a feasible diagnostic tool for clinical use. It should be noted that the proposed CAD system is presented as a tool for experts—not a replacement for them. The primary motivation of this thesis is the design of a system that could act as a catalyst for reducing the mortality rate associated with 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
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