Using Deep Learning for Pulmonary Nodule Detection & Diagnosis

This study uses a revolutionary image recognition method, deep learning, for the classification of potentially malignant pulmonary nodules. Deep learning is based on deep neural networks. We report results of our initial findings and compare performance of deep neural nets using a combination of different network topologies and optimization parameters. Classification accuracy, sensitivity and specificity of the network performance are assessed for each of the four topologies

CT lung images segmentation using image processing and Markov random field

Introduction: In this study, the performance of computed tomography lung image segmentation using image processing and Markov Random Field was investigated. Before cancer segmentation and analysis, lung segmentation is an important initial process. Thus, the aim of this study is to find the optimal Markov Random Field setting for lung segmentation. Methods: The Centre for Diagnostic Nuclear Imaging at UPM provided 11 anonymous sets of cancerous lung CT images for this study. The thresholding technique is an effective method for medical image segmentation when the priori information for the region of interest is known, such as the Hounsfield Unit value of lung. Due to the large differences in grey levels in the image, the thresholding approach is difficult to apply in segmentation, especially for lung. Thus, for the segmentation process, this study used multilevel thresholding with Markov Random Field with three settings; Iterated Condition Mode, Metropolis algorithm, and Gibbs sampler. The images then went through image processing procedures which were binarization, small object removal, lung region extraction and lung segmentation. The output from the experiments were analyzed and compared to determine the ideal lung segmentation setting. Results: The Jaccard index average values; Markov Random Field -Metropolis = 0.9464, Markov Random Field -ICM = 0.9499 and Markov Random Field -Gibbs = 0.9512. The Dice index average values; Markov Random Field - Metropolis = 0.9743, Markov Random Field - ICM = 0.9724 and Markov Random Field - Gibbs = 0.9749. Conclusion: Markov Random Field using Gibbs sampler delivered the best results for lung segmentation

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

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