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

By S. Armato, Geoffrey Mclennan, L. Bidaut, M. McNitt-Gray, C. R. Meyer, Anthony P. Reeves, Binsheng Zhao, Denise Aberle, C. Henschke, Eric A. Hoffman and 46 more. Medical Physics (Lancaster), 2011

2,915 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8257167186837ff6840d6c2f552b4d23ff26ec81

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Pulmonary and Respiratory Medicine

1. B. van Ginneken
2. Hugo J. W. L. Aerts
3. Philippe Lambin
4. Qi-Yan Dou
5. Weidong (Tom) Cai
6. D. Feng
7. Xiaohui S. Xie
8. P. Heng

In Radiology, Nuclear Medicine and Imaging

1. B. van Ginneken
2. P. Heng
3. Lequan Yu
4. Qi-Yan Dou
5. Hao Chen
6. Jing Qin
7. Hugo J. W. L. Aerts
8. Philippe Lambin

Citations in the corpus

• A review of medical image data augmentation techniques for deep learning applications (2021, methodology): “Examples of data augmentation are provided where possible and include data from the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI), 12 which consists of lung cancer diagnostic and screening CT scans along with annotations of nodules.”
• RIM-ONE: An open retinal image database for optic nerve evaluation (2011, background): “In the last years some efforts have been made in this direction and some reference image databases have been proposed for several medical fields: lung nodules images [4], cancer images [5], cardiovascular images [6], Magnetic Resonance images (MRI) [7], among others.”
• A Probabilistic U-Net for Segmentation of Ambiguous Images (2018, background): “from each of them. 4 Results A quantitative evaluation of multiple segmentation predictions per image requires annotations from multiple labelers. Here we consider two datasets: The LIDC-IDRI dataset [32, 33, 34] which contains 4 annotations per input, and the Cityscapes dataset [35], which we artifi”
• Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images (2020, methodology): “If the lung abnormality is initially assessed by a computer-assisted technique, then the analysis report can be submitted to the pulmonologist to support the decision making and the treatment planning process [8-12].”
• A Review of Deep-Learning-Based Medical Image Segmentation Methods (2021, methodology): “For the images in each example, four experienced thoracic radiologists performed a two-stage diagnosis and annotation [110].”
• Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis (2019, methodology): “ned Models Genesis. 20 Fig.11: Qualitative assessment of image restoration quality by Genesis Chest CT across dataset, organ, and modality is visualized. Genesis Chest CT is trained on LIDC-IDRI (CT) [1] via our unied self-supervised training framework. For testing, we use the pre-trained model to d”
• Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network (2017, positive): “During 2011-2015, a much larger dataset LIDC [18, 19, 20] was developed.”
• Med3D: Transfer Learning for 3D Medical Image Analysis (2019, methodology): “LIDC dataset [40, 17] collected thoracic CT scans from 1,010 patients and nodules of each CT scan were annotated by four radiologists.”
• Solving inverse problems using data-driven models (2019, background): “7 on the LIDC/IDRI database (Armato et al. 2011) of lung scans.”
• Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification (2017, methodology): “Related studies on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset [21] are shown in Table 1.”
• Lung Nodule Classification Using Deep Features in CT Images (2015, background): “LIDC (Lung Cancer Data Consortium) [5], [6] is one such database which contains CT images of thoracic region for 1010 patients along with the annotation data of suspicious nodules (for both benign and malignant cases) for a size greater than 3 mm from upto four radiologists collected over a long…”
• The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, background): “For instance, inter-reader variability of the interpretation for in-plane nodule boundaries could be very large [54].”