ArticleJournal of medical systems2015
Automatic lung segmentation using control feedback system: morphology and texture paradigm.
Article in Journal of medical systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 80 citations in OpenAlex.
- Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective.IEEE journal of biomedical and health informatics · 2021Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.Journal of cardiovascular development and disease · 2023Review
- Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm.Diagnostics (Basel, Switzerland) · 2023Article
- Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.Journal of cardiovascular development and disease · 2022Review
- Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.Computers in biology and medicine · 2022Article
- COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans.Diagnostics (Basel, Switzerland) · 2022Article
- Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.Cancers · 2022Review
- COVLIAS 1.0Diagnostics (Basel, Switzerland) · 2022Article
- COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts.Diagnostics (Basel, Switzerland) · 2021Article
- COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models.Diagnostics (Basel, Switzerland) · 2021Article
- A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.Computers in biology and medicine · 2021Review
- A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort.Journal of medical systems · 2021Article
- Automatic Segmentation of Pathological Glomerular Basement Membrane in Transmission Electron Microscopy Images with Random Forest Stacks.Computational and mathematical methods in medicine · 2019Article
- A Survey on Coronary Atherosclerotic Plaque Tissue Characterization in Intravascular Optical Coherence Tomography.Current atherosclerosis reports · 2018Review
- Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm.Journal of medical systems · 2017Article
- An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images.Journal of medical systems · 2017Article
- High attenuation areas on chest computed tomography in community-dwelling adults: the MESA study.The European respiratory journal · 2016Article
- HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images.Journal of medical systems · 2016Article
- Inter-observer Variability Analysis of Automatic Lung Delineation in Normal and Disease Patients.Journal of medical systems · 2016Article
Corrections and comments
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Authors and funding
9 authors at 7 institutions in 3 countries.
Funding
Abstract
Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung's performance of segmentation was 96.52% for Jaccard Index and 98.21% for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), -1.15% for Relative Area Error and 4.09% Area Overlap Error. The right lung's performance of segmentation was 97.24% for Jaccard Index, 98.58% for Dice Similarity, 0.61 mm for PDM, -0.03% for Relative Area Error and 3.53% for Area Overlap Error. The segmentation overall has an overall similarity of 98.4%. The segmentation proposed is an accurate and fully automated system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.