3D U-net for registration of lung nodules in longitudinal CT scans

2021 ◽  
Author(s):  
Aryan Ghazipour ◽  
Benjamin Veasey ◽  
Albert Seow ◽  
Amir Amini
Keyword(s):  
Ct Scans ◽  
Lung Nodules

scholarly journals Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Lea Pehrson ◽  
Michael Nielsen ◽  
Carsten Ammitzbøl Lauridsen
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Ct Scans ◽  
Lung Nodules ◽  
Original Research ◽  
Feature Based ◽  
High Level ◽  
Meta Analyses

The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT scans. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning algorithms applied to the LIDC-IDRI database were included. The initial search yielded 1972 publications after removing duplicates, and 41 of these articles were included in this study. The articles were divided into two subcategories describing their overall architecture. The majority of feature-based algorithms achieved an accuracy >90% compared to the deep learning (DL) algorithms that achieved an accuracy in the range of 82.2%–97.6%. In conclusion, ML and DL algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity using ML, when applied to an annotated archive of CT scans of the lung. However, there is no consensus on the method applied to determine the efficiency of ML algorithms.

scholarly journals Computer-Aided Diagnosis of Lung Nodules on CT Scans:

2010 ◽  
Vol 17 (3) ◽  
pp. 323-332 ◽  
Author(s):  
Ted Way ◽  
Heang-Ping Chan ◽  
Lubomir Hadjiiski ◽  
Berkman Sahiner ◽  
Aamer Chughtai ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Ct Scans ◽  
Lung Nodules ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals The Lung Image Database Consortium (LIDC): An Evaluation of Radiologist Variability in the Identification of Lung Nodules on CT Scans

2007 ◽  
Vol 14 (11) ◽  
pp. 1409-1421 ◽  
Author(s):  
Samuel G. Armato ◽  
Michael F. McNitt-Gray ◽  
Anthony P. Reeves ◽  
Charles R. Meyer ◽  
Geoffrey McLennan ◽  
...  
Keyword(s):  
Image Database ◽  
Ct Scans ◽  
Lung Nodules

Algorithms for automatic detection of lung nodules in CT scans

2011 ◽  
Author(s):  
Maria Evelina Fantacci ◽  
Niccolo Camarlinghi ◽  
Roberto Bellotti ◽  
Gianfranco Gargano ◽  
Rosario Megna ◽  
...  
Keyword(s):  
Automatic Detection ◽  
Ct Scans ◽  
Lung Nodules

Evolution of radiomic features on serial CT scans as an imaging based biomarker for evaluating response in patients with non-small cell lung cancer treated with nivolumab.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e14534-e14534 ◽  
Author(s):  
Yuanqi Xie ◽  
Monica Khunger ◽  
Rajat Thawani ◽  
Vamsidhar Velcheti ◽  
Anant Madabhushi
Keyword(s):  
Clinical Benefit ◽  
Texture Features ◽  
Unsupervised Clustering ◽  
Post Treatment ◽  
Response To Treatment ◽  
Ct Scans ◽  
Lung Nodules ◽  
Clinical Approach ◽  
Current Standard ◽  
Learning Set

e14534 Background: Nivolumab is a PD-1 inhibitor that is FDA approved for treatment of chemotherapy refractory advanced NSCLC. The current standard clinical approach to evaluating tumor response is sub-optimal in evaluating clinical benefit from immunotherapy drugs. Our study aims to explore whether changes in radiomic features of the tumor between baseline and 2-week post-treatment CT scans can predict treatment response. Methods: 41 NSCLC patients treated with nivolumab were included in this study. 22 patients with pre- and post-nivolumab CT scans were used as a learning set and the remaining 19 for independent testing. Patients who did not receive nivolumab after 2 cycles due to lack of response or progression as per RECIST were classified as ‘non-responders’, and patients who had radiological response as per RECIST, or stable disease as per RECIST and clinical improvement were classified as ‘responders’. Lung nodules on pre-treatment CT scans were annotated with 3D SLICER software by a radiologist. 312 texture features of lung nodules were extracted and investigated in the study. The percent difference of each extracted feature was calculated based on the baseline and 2 week post-therapy CT scan. In the learning set, the six features that most significantly changed between baseline and post-treatment scans and also maximally differentially expressed between responders and non-responders were identified. Unsupervised clustering was applied on the set of 6 features for the 19 patients in the test set to predict which patients did and did not respond. Results: The top 6 features predictive of response corresponded to the Haralick, Gabor and Laws texture families. Unsupervised clustering yielded an accuracy of 78.95%. Conclusions: Our results suggest that changes in certain radiomic texture features between baseline and post-treatment CT scans following nivolumab could identify early clinical response to treatment. Additional validation of these novel quantitative imaging based approaches is warranted to accurately define clinical benefit from immunotherapy.

scholarly journals Discriminative Random Field Segmentation of Lung Nodules in CT Studies

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Brian Liu ◽  
Ashish Raj
Keyword(s):  
Random Fields ◽  
Ct Scans ◽  
Lung Nodules ◽  
Optimal Parameters ◽  
3D Segmentation ◽  
High Quality ◽  
Inference Problem ◽  
Seed Point ◽  
Scan Data ◽  
Ct Studies

The ability to conduct high-quality semiautomatic 3D segmentation of lung nodules in CT scans is of high value to busy radiologists. Discriminative random fields (DRFs) were used to segment 3D volumes of lung nodules in CT scan data using only one seed point per nodule. Optimal parameters for the DRF inference were first found using simulated annealing. These parameters were then used to solve the inference problem using the graph cuts algorithm. Results of the segmentation exhibited high precision and recall. The system can be adapted to facilitate the process of longitudinal studies but will still require human checking for failed cases.

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