The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

India is experiencing a rapid rise in lung cancer cases, with more than 1 lakh new cases forecast in the next five years. 90% of patients who are not discovered early live for five years, however, only 10-15% of patients who are discovered late live for five years. Lung cancer can be discovered early with computed tomography (CT) imaging. To detect and classify lung nodules and their malignancy levels, Computer Tomography (CT) scans were used on lung patients. Since nodules vary widely in size, shape, and texture, detecting them is a difficult task. Moreover, there are other structures in the lungs that are considered to be non-nodules, for example vessels, fibrosis, diffusive diseases, which often resemble nodules in appearance. Therefore, proposed an end-to-end architecture to recognize nodules from three-dimensional CT volume by modified UNet segmentation and CNN based classification on readily available Lung Image Database Consortium – Image Database Resource Initiative (LIDC-IDRI), virtuously self-governing Indian Lung CT Image Database (ILCID) and LNDb Challenge Dataset. According to the results obtained with the proposed method, nodules can be reliably detected and segmented in contrast to the existing lung segmentation, classification and detection algorithms. For nodule segmentation, attained a Dice Similarity Coefficient (DSC) value is about 0.89 and for nodule detection, achieved the Sensitivity of 98.53%.

LNCDS: A 2D-3D cascaded CNN approach for lung nodule classification, detection and segmentation

To stimulate the advancement of computer-aided diagnostic (CAD) research for lung nodules in thoracic computed tomography (CT), the National Cancer Institute launched a cooperative effort known as the Lung Image Database Consortium (LIDC). The LIDC is composed of five academic institutions from across the United States that are working together to develop an image database that will serve as an international research resource for the development, training, and evaluation of CAD methods in the detection of lung nodules on CT scans. Prior to the collection of CT images and associated patient data, the LIDC has been engaged in a consensus process to identify, address, and resolve a host of challenging technical and clinical issues to provide a solid foundation for a scientifically robust database. These issues include the establishment of (a) a governing mission statement, (b) criteria to determine whether a CT scan is eligible for inclusion in the database, (c) an appropriate definition of the term qualifying nodule, (d) an appropriate definition of "truth" requirements, (e) a process model through which the database will be populated, and (f) a statistical framework to guide the application of assessment methods by users of the database. Through a consensus process in which careful planning and proper consideration of fundamental issues have been emphasized, the LIDC database is expected to provide a powerful resource for the medical imaging research community. This article is intended to share with the community the breadth and depth of these key issues.

Development of a Personalized Training System Using the Lung Image Database Consortium and Image Database Resource Initiative Database

Improving the performance of lung nodule classification by fusing structured and unstructured data

Attention-guided deep neural network with a multichannel architecture for lung nodule classification

The US Food and Drug Administration Centers for Regulatory Science and Innovation: Current Activities and Future Promise to Accelerate Innovations.

Lung cancer is the leading cause of cancer death in the United States. It usually exhibits its presence with the formation of pulmonary nodules. Nodules are round or oval-shaped growth present in the lung. Computed Tomography (CT) scans are used by radiologists to detect such nodules. Computer Aided Detection (CAD) of such nodules would aid in providing a second opinion to the radiologists and would be of valuable help in lung cancer screening. In this research, we study various feature selection methods for the CAD system framework proposed in FlyerScan. Algorithmic steps of FlyerScan include (i) local contrast enhancement (ii) automated anatomical segmentation (iii) detection of potential nodule candidates (iv) feature computation & selection and (v) candidate classification. In this paper, we study the performance of the FlyerScan by implementing various classification methods such as linear, quadratic and Fischer linear discriminant classifier. This algorithm is implemented using a publicly available Lung Image Database Consortium — Image Database Resource Initiative (LIDC-IDRI) dataset. 107 cases from LIDC-IDRI are handpicked in particular for this paper and performance of the CAD system is studied based on 5 example cases of Automatic Nodule Detection (ANODE09) database. This research will aid in improving the nodule detection rate in CT scans, thereby enhancing a patient's chance of survival.

Measuring Interobserver Disagreement in Rating Diagnostic Characteristics of Pulmonary Nodule Using the Lung Imaging Database Consortium and Image Database Resource Initiative

Computer Aided Diagnosis (CAD) systems for lung nodules diagnosis aim to classify nodules into benign or malignant based on images obtained from diverse imaging modalities such as Computer Tomography (CT). Automated CAD systems are important in medical domain applications as they assist radiologists in the time-consuming and labor-intensive diagnosis process. However, most available methods require a large collection of nodules that are segmented and annotated by radiologists. This process is labor-intensive and hard to scale to very large datasets. More recently, some CAD systems that are based on deep learning have emerged. These algorithms do not require the nodules to be segmented, and radiologists need to only provide the center of mass of each nodule. The training image patches are then extracted from volumes of fixed-sized centered at the provided nodule's center. However, since the size of nodules can vary significantly, one fixed size volume may not represent all nodules effectively. This thesis proposes a Multiple Instance Learning (MIL) approach to address the above limitations. In MIL, each nodule is represented by a nested sequence of volumes centered at the identified center of the nodule. We extract one feature vector from each volume. The set of features for each nodule are combined and represented by a bag. Next, we investigate and adapt some existing algorithms and develop new ones for this application. We start by applying benchmark MIL algorithms to traditional Gray Level Co-occurrence Matrix (GLCM) engineered features. Then, we design and train simple Convolutional Neural Networks (CNNs) to learn and extract features that characterize lung nodules. These extracted features are then fed to a benchmark MIL algorithm to learn a classification model. Finally, we develop new algorithms (MIL-CNN) that combine feature learning and multiple instance classification in a single network. These algorithms generalize the CNN architecture to multiple instance data. We design and report the results of three experiments applied on both generative (GLCM) and learned (CNN) features using two datasets (The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) \cite{armato2011lung} and the National Lung Screening Trial (NLST) \cite{national2011reduced}). Two of these experiments perform five-fold cross-validations on the same dataset (NLST or LIDC). The third experiment trains the algorithms on one collection (NLST dataset) and tests it on the other (LIDC dataset). We designed our experiments to compare the different features, compare MIL versus Single Instance Learning (SIL) where a single feature vector represents a nodule, and

In recent years, so many Computer Aided Diagnosis (CAD) systems are designed for diagnosis of several diseases. Lung cancer detection at early stage has become very important and also very easy with image processing and deep learning techniques. In this study lung patient Computer Tomography (CT) scan images are used to detect and classify the lung nodules and to detect the malignancy level of that nodules. The CT scan images are segmented using U-Net architecture. This paper proposes 3D multipath VGG-like network, which is evaluated on 3D cubes, extracted from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), LUng Nodule Analysis 2016 (LUNA16) and Kaggle Data Science Bowl 2017 datasets. Prediction from U-Net and 3D multipath VGG-like network are combined for final results. The lung nodules are classified and malignancy level is detected using this architecture with 95.60% of Accuracy and 0.387732 of logloss.

The Lung Image Database Consortium (LIDC): Ensuring the Integrity of Expert-Defined “Truth”

An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung CT scans

We investigate the problem of diagnostic lung nodule classification using thoracic Computed Tomography (CT) screening. Unlike traditional studies primarily relying on nodule segmentation for regional analysis, we tackle a more challenging problem on directly modelling raw nodule patches without any prior definition of nodule morphology. We propose a hierarchical learning framework--Multi-scale Convolutional Neural Networks (MCNN)--to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. In particular, to sufficiently quantify nodule characteristics, our framework utilizes multi-scale nodule patches to learn a set of class-specific features simultaneously by concatenating response neuron activations obtained at the last layer from each input scale. We evaluate the proposed method on CT images from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), where both lung nodule screening and nodule annotations are provided. Experimental results demonstrate the effectiveness of our method on classifying malignant and benign nodules without nodule segmentation.

Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computed Tomography (CT) chest scans can provide early treatment for patients with lung cancer, as well as doctor liberation from time-consuming procedures. The purpose of this study is the automatic and reliable characterization of SPNs in CT scans extracted from Positron Emission Tomography and Computer Tomography (PET/CT) system. To achieve the aforementioned task, Deep Learning with Convolutional Neural Networks (CNN) is applied. The strategy of training specific CNN architectures from scratch and the strategy of transfer learning, by utilizing state-of-the-art pre-trained CNNs, are compared and evaluated. To enhance the training sets, data augmentation is performed. The publicly available database of CT scans, named as Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), is also utilized to further expand the training set and is added to the PET/CT dataset. The results highlight the effectiveness of transfer learning and data augmentation for the classification task of small datasets. The best accuracy obtained on the PET/CT dataset reached 94%, utilizing a modification proposal of a state-of-the-art CNN, called VGG16, and enhancing the training set with LIDC-IDRI dataset. Besides, the proposed modification outperforms in terms of sensitivity several similar researches, which exploit the benefits of transfer learning. Overview of the experiment setup. The two datasets containing nodule representations are combined to evaluate the effectiveness of transfer learning over the traditional approach of training Convolutional Neural Networks from scratch.