Automatic Lung Segmentation Using Control Feedback System: Morphology and Texture Paradigm

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.

Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial

“…The combined cumulative distribution of cIMT error curves for (i) AtheroCloud™ vs. manual, (ii) sonographer vs. manual (iii) AtheroCloud™ vs. sonographer is shown in Figure 10. Keeping the cIMT error threshold as 10% of the cIMT thickness (i.e., nearly 0.11 mm), we observed that 91.15%, 68.31% and 53.91% of population met the error threshold criteria for three error curves.…”

“…This is beyond the scope for this pilot study. We have however adapted a standardized software (ImgTracer™, AtheroPoint, Roseville, CA, USA), which has been applied to several studies in medical imaging [68][69][70][71][72][73]. …”

Extracting Lungs from CT Images Using Fully Convolutional Networks

“…Evaluation in 233 CT scans reported a inclusion rate of 92.6% (373 out of 406 juxtapleural nodules), with averages of under-and oversegmentation of 2.4% and 0.3% respectively. Also, a control feedback system based segmentation using morphology and texture was proposed in [15] for analysis of 96 patients, which 81 had presence of ILDs. This approach obtained a Dice coefficient of 98.21% ± 1.35% and 98.58% ± 1.28% for left and right lungs respectively, with an average of 95.40% for both lungs.…”

A Visually Guided Framework for Lung Segmentation and Visualization in Chest CT Images

“…Hence, a fast and intuitive lung segmentation and visualization method could have remarkable practical significance and clinical value for optimize screening efficiency. Thus far, many lung segmentation methods have been studied by researchers, see [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] and references therein. Here, we classify them simply into two classes: the automatic segmentation methods and the interactive segmentation methods.…”