854 research outputs found
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
Early detection of pulmonary cancer is the most promising way to enhance a
patient's chance for survival. Accurate pulmonary nodule detection in computed
tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In
this paper, inspired by the successful use of deep convolutional neural
networks (DCNNs) in natural image recognition, we propose a novel pulmonary
nodule detection approach based on DCNNs. We first introduce a deconvolutional
structure to Faster Region-based Convolutional Neural Network (Faster R-CNN)
for candidate detection on axial slices. Then, a three-dimensional DCNN is
presented for the subsequent false positive reduction. Experimental results of
the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior
detection performance of the proposed approach on nodule detection(average
FROC-score of 0.891, ranking the 1st place over all submitted results).Comment: MICCAI 2017 accepte
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
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
Automatic 3D pulmonary nodule detection in CT images: a survey
This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks
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