LNDb: A Lung Nodule Database on Computed Tomography
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly, time-consuming and prone to variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to clinical routine is challenging. In this study, a new database for the development and testing of pulmonary nodule computer-aided strategies is presented which intends to complement current databases by giving additional focus to radiologist variability and local clinical reality. State-of-the-art nodule detection, segmentation and characterization methods are tested and compared to manual annotations as well as collaborative strategies combining multiple radiologists and radiologists and computer-aided systems. It is shown that state-of-the-art methodologies can determine a patient's follow-up recommendation as accurately as a radiologist, though the nodule detection method used shows decreased performance in this database.
Code (0)
등록된 구현이 없습니다.
Tasks
SegmentationSimilar Papers 제목 키워드 기반
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
Computed tomography imaging is a standard modality for detecting and assessing lung cancer. In order to evaluate the malignancy of lung nodules, clinical practice often involves expert qualitative ratings on several crit…
Lung Cancer DiagnosisDeep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung
In this work we present a method for lung nodules segmentation, their texture classification and subsequent follow-up recommendation from the CT image of lung. Our method consists of neural network model based on popular…
ClassificationGeneral ClassificationSegmentationTexture ClassificationA Cross Spatio-Temporal Pathology-based Lung Nodule Dataset
Recently, intelligent analysis of lung nodules with the assistant of computer aided detection (CAD) techniques can improve the accuracy rate of lung cancer diagnosis. However, existing CAD systems and pulmonary datasets …
Computed Tomography (CT)Lung Cancer DiagnosisEnd-to-end Lung Nodule Detection in Computed Tomography
Computer aided diagnostic (CAD) system is crucial for modern med-ical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Computer vision can capture features that …
Computed Tomography (CT)DiagnosticLung Nodule DetectionBenign-Malignant Lung Nodule Classification with Geometric and Appearance Histogram Features
Lung cancer accounts for the highest number of cancer deaths globally. Early diagnosis of lung nodules is very important to reduce the mortality rate of patients by improving the diagnosis and treatment of lung cancer. T…
Computed Tomography (CT)DiagnosticGeneral ClassificationLung Nodule Classification