1,084 research outputs found
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 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
Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding
Objective: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels
Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification
Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain. In this review, different types of biases that may exist in chest CT AI nodule detection and classification algorithms are listed and discussed. Examples from the literature in which each type of bias occurs are presented, along with ways to mitigate these biases. Different types of biases can occur in chest CT AI algorithms for lung nodule detection and classification. Mitigation of them can be very difficult, if not impossible to achieve completely
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
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
- …