3,826 research outputs found
Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models
We propose a computer-aided detection (CAD) system which can detect small-sized (from 3 mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass–Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules
Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks
In this paper, we investigate the effectiveness of deep learning techniques
for lung nodule classification in computed tomography scans. Using less than
10,000 training examples, our deep networks perform two times better than a
standard radiology software. Visualization of the networks' neurons reveals
semantically meaningful features that are consistent with the clinical
knowledge and radiologists' perception. Our paper also proposes a novel
framework for rapidly adapting deep networks to the radiologists' feedback, or
change in the data due to the shift in sensor's resolution or patient
population. The classification accuracy of our approach remains above 80% while
popular deep networks' accuracy is around chance. Finally, we provide in-depth
analysis of our framework by asking a radiologist to examine important
networks' features and perform blind re-labeling of networks' mistakes
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning
There are at least two categories of errors in radiology screening that can
lead to suboptimal diagnostic decisions and interventions:(i)human fallibility
and (ii)complexity of visual search. Computer aided diagnostic (CAD) tools are
developed to help radiologists to compensate for some of these errors. However,
despite their significant improvements over conventional screening strategies,
most CAD systems do not go beyond their use as second opinion tools due to
producing a high number of false positives, which human interpreters need to
correct. In parallel with efforts in computerized analysis of radiology scans,
several researchers have examined behaviors of radiologists while screening
medical images to better understand how and why they miss tumors, how they
interact with the information in an image, and how they search for unknown
pathology in the images. Eye-tracking tools have been instrumental in exploring
answers to these fundamental questions. In this paper, we aim to develop a
paradigm shift CAD system, called collaborative CAD (C-CAD), that unifies both
of the above mentioned research lines: CAD and eye-tracking. We design an
eye-tracking interface providing radiologists with a real radiology reading
room experience. Then, we propose a novel algorithm that unifies eye-tracking
data and a CAD system. Specifically, we present a new graph based clustering
and sparsification algorithm to transform eye-tracking data (gaze) into a
signal model to interpret gaze patterns quantitatively and qualitatively. The
proposed C-CAD collaborates with radiologists via eye-tracking technology and
helps them to improve diagnostic decisions. The C-CAD learns radiologists'
search efficiency by processing their gaze patterns. To do this, the C-CAD uses
a deep learning algorithm in a newly designed multi-task learning platform to
segment and diagnose cancers simultaneously.Comment: Submitted to Medical Image Analysis Journal (MedIA
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
In this work, we present a fully automated lung computed tomography (CT)
cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule
detection (identifying the locations of candidate nodules) and classification
(classifying candidate nodules into benign or malignant). Considering the 3D
nature of lung CT data and the compactness of dual path networks (DPN), two
deep 3D DPN are designed for nodule detection and classification respectively.
Specifically, a 3D Faster Regions with Convolutional Neural Net (R-CNN) is
designed for nodule detection with 3D dual path blocks and a U-net-like
encoder-decoder structure to effectively learn nodule features. For nodule
classification, gradient boosting machine (GBM) with 3D dual path network
features is proposed. The nodule classification subnetwork was validated on a
public dataset from LIDC-IDRI, on which it achieved better performance than
state-of-the-art approaches and surpassed the performance of experienced
doctors based on image modality. Within the DeepLung system, candidate nodules
are detected first by the nodule detection subnetwork, and nodule diagnosis is
conducted by the classification subnetwork. Extensive experimental results
demonstrate that DeepLung has performance comparable to experienced doctors
both for the nodule-level and patient-level diagnosis on the LIDC-IDRI
dataset.\footnote{https://github.com/uci-cbcl/DeepLung.git}Comment: 9 pages, 8 figures, IEEE WACV conference. arXiv admin note:
substantial text overlap with arXiv:1709.0553
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
Automatic diagnosing lung cancer from Computed Tomography (CT) scans involves
two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the
whole-lung/pulmonary malignancy. Currently, there are many studies about the
first step, but few about the second step. Since the existence of nodule does
not definitely indicate cancer, and the morphology of nodule has a complicated
relationship with cancer, the diagnosis of lung cancer demands careful
investigations on every suspicious nodule and integration of information of all
nodules. We propose a 3D deep neural network to solve this problem. The model
consists of two modules. The first one is a 3D region proposal network for
nodule detection, which outputs all suspicious nodules for a subject. The
second one selects the top five nodules based on the detection confidence,
evaluates their cancer probabilities and combines them with a leaky noisy-or
gate to obtain the probability of lung cancer for the subject. The two modules
share the same backbone network, a modified U-net. The over-fitting caused by
the shortage of training data is alleviated by training the two modules
alternately. The proposed model won the first place in the Data Science Bowl
2017 competition. The code has been made publicly available.Comment: 12 pages, 9 figure
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
To predict lung nodule malignancy with a high sensitivity and specificity, we
propose a fusion algorithm that combines handcrafted features (HF) into the
features learned at the output layer of a 3D deep convolutional neural network
(CNN). First, we extracted twenty-nine handcrafted features, including nine
intensity features, eight geometric features, and twelve texture features based
on grey-level co-occurrence matrix (GLCM) averaged from thirteen directions. We
then trained 3D CNNs modified from three state-of-the-art 2D CNN architectures
(AlexNet, VGG-16 Net and Multi-crop Net) to extract the CNN features learned at
the output layer. For each 3D CNN, the CNN features combined with the 29
handcrafted features were used as the input for the support vector machine
(SVM) coupled with the sequential forward feature selection (SFS) method to
select the optimal feature subset and construct the classifiers. The fusion
algorithm takes full advantage of the handcrafted features and the highest
level CNN features learned at the output layer. It can overcome the
disadvantage of the handcrafted features that may not fully reflect the unique
characteristics of a particular lesion by combining the intrinsic CNN features.
Meanwhile, it also alleviates the requirement of a large scale annotated
dataset for the CNNs based on the complementary of handcrafted features. The
patient cohort includes 431 malignant nodules and 795 benign nodules extracted
from the LIDC/IDRI database. For each investigated CNN architecture, the
proposed fusion algorithm achieved the highest AUC, accuracy, sensitivity, and
specificity scores among all competitive classification models.Comment: 11 pages, 5 figures, 5 tables. This work has been submitted to the
IEEE for possible publicatio
DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations
Extracting, harvesting and building large-scale annotated radiological image
datasets is a greatly important yet challenging problem. It is also the
bottleneck to designing more effective data-hungry computing paradigms (e.g.,
deep learning) for medical image analysis. Yet, vast amounts of clinical
annotations (usually associated with disease image findings and marked using
arrows, lines, lesion diameters, segmentation, etc.) have been collected over
several decades and stored in hospitals' Picture Archiving and Communication
Systems. In this paper, we mine and harvest one major type of clinical
annotation data - lesion diameters annotated on bookmarked images - to learn an
effective multi-class lesion detector via unsupervised and supervised deep
Convolutional Neural Networks (CNN). Our dataset is composed of 33,688
bookmarked radiology images from 10,825 studies of 4,477 unique patients. For
every bookmarked image, a bounding box is created to cover the target lesion
based on its measured diameters. We categorize the collection of lesions using
an unsupervised deep mining scheme to generate clustered pseudo lesion labels.
Next, we adopt a regional-CNN method to detect lesions of multiple categories,
regardless of missing annotations (normally only one lesion is annotated,
despite the presence of multiple co-existing findings). Our integrated mining,
categorization and detection framework is validated with promising empirical
results, as a scalable, universal or multi-purpose CAD paradigm built upon
abundant retrospective medical data. Furthermore, we demonstrate that detection
accuracy can be significantly improved by incorporating pseudo lesion labels
(e.g., Liver lesion/tumor, Lung nodule/tumor, Abdomen lesions, Chest lymph node
and others). This dataset will be made publicly available (under the open
science initiative)
CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning
In 2018, clinics and hospitals were hit with numerous attacks leading to
significant data breaches and interruptions in medical services. An attacker
with access to medical records can do much more than hold the data for ransom
or sell it on the black market.
In this paper, we show how an attacker can use deep-learning to add or remove
evidence of medical conditions from volumetric (3D) medical scans. An attacker
may perform this act in order to stop a political candidate, sabotage research,
commit insurance fraud, perform an act of terrorism, or even commit murder. We
implement the attack using a 3D conditional GAN and show how the framework
(CT-GAN) can be automated. Although the body is complex and 3D medical scans
are very large, CT-GAN achieves realistic results which can be executed in
milliseconds.
To evaluate the attack, we focused on injecting and removing lung cancer from
CT scans. We show how three expert radiologists and a state-of-the-art deep
learning AI are highly susceptible to the attack. We also explore the attack
surface of a modern radiology network and demonstrate one attack vector: we
intercepted and manipulated CT scans in an active hospital network with a
covert penetration test.
Demo video: https://youtu.be/_mkRAArj-x0
Source code: https://github.com/ymirsky/CT-GANComment: This paper is included in the Proceedings of the 28th USENIX Security
Symposium (USENIX Security 2019
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Age, comorbidity, life expectancy, and pulmonary nodule follow-up in older veterans.
BackgroundPulmonary nodule guidelines do not indicate how to individualize follow-up according to comorbidity or life expectancy.ObjectivesTo characterize comorbidity and life expectancy in older veterans with incidental, symptom-detected, or screen-detected nodules in 2008-09 compared to 2013-14. To determine the impact of these patient factors on four-year nodule follow-up among the 2008-09 subgroup.DesignRetrospective cohort study.SettingUrban Veterans Affairs Medical Center.Participants243 veterans age ≥65 with newly diagnosed pulmonary nodules in 2008-09 (followed for four years through 2012 or 2013) and 446 older veterans diagnosed in 2013-14.MeasurementsThe primary outcome was receipt of any follow-up nodule imaging and/or biopsy within four years after nodule diagnosis. Primary predictor variables included age, Charlson-Deyo Comorbidity Index (CCI), and life expectancy. Favorable life expectancy was defined as age 65-74 with CCI 0 while limited life expectancy was defined as age ≥85 with CCI ≥1 or age ≥65 with CCI ≥4. Interaction by nodule size was also examined.ResultsFrom 2008-09 to 2013-14, the number of older veterans diagnosed with new pulmonary nodules almost doubled, including among those with severe comorbidity and limited life expectancy. Overall among the 2008-09 subgroup, receipt of nodule follow-up decreased with increasing comorbidity (CCI ≥4 versus 0: adjusted RR 0.61, 95% CI 0.39-0.95) with a trend towards decreased follow-up among those with limited life expectancy (adjusted RR 0.69, 95% CI 0.48-1.01). However, we detected an interaction effect with nodule size such that comorbidity and life expectancy were associated with decreased follow-up only among those with nodules ≤6 mm.ConclusionsWe found some individualization of pulmonary nodule follow-up according to comorbidity and life expectancy in older veterans with smaller nodules only. As increased imaging detects nodules in sicker patients, guidelines need to be more explicit about how to best incorporate comorbidity and life expectancy to maximize benefits and minimize harms for patients with nodules of all sizes
Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning from Radiology Reports and Label Ontology
In radiologists' routine work, one major task is to read a medical image,
e.g., a CT scan, find significant lesions, and describe them in the radiology
report. In this paper, we study the lesion description or annotation problem.
Given a lesion image, our aim is to predict a comprehensive set of relevant
labels, such as the lesion's body part, type, and attributes, which may assist
downstream fine-grained diagnosis. To address this task, we first design a deep
learning module to extract relevant semantic labels from the radiology reports
associated with the lesion images. With the images and text-mined labels, we
propose a lesion annotation network (LesaNet) based on a multilabel
convolutional neural network (CNN) to learn all labels holistically.
Hierarchical relations and mutually exclusive relations between the labels are
leveraged to improve the label prediction accuracy. The relations are utilized
in a label expansion strategy and a relational hard example mining algorithm.
We also attach a simple score propagation layer on LesaNet to enhance recall
and explore implicit relation between labels. Multilabel metric learning is
combined with classification to enable interpretable prediction. We evaluated
LesaNet on the public DeepLesion dataset, which contains over 32K diverse
lesion images. Experiments show that LesaNet can precisely annotate the lesions
using an ontology of 171 fine-grained labels with an average AUC of 0.9344.Comment: CVPR 2019 oral, main paper + supplementary materia
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