Computer‐aided detection and quantification of cavitary tuberculosis from CT scans

Abstract

Purpose: To present a computer-aided detection tool for identifying, quantifying, and evaluating tuberculosis (TB) cavities in the infected lungs from computed tomography (CT) scans. Methods: The authors' proposed method is based on a novel shape-based automated detection algorithm on CT scans followed by a fuzzy connectedness (FC) delineation procedure. In order to assess interaction between cavities and airways, the authors first roughly identified air-filled structures (airway, cavities, esophagus, etc.) by thresholding over Hounsfield unit of CT image. Then, airway and cavity structure detection was conducted within the support vector machine classification algorithm. Once airway and cavities were detected automatically, the authors extracted airway tree using a hybrid multiscale approach based on novel affinity relations within the FC framework and segmented cavities using intensity-based FC algorithm. At final step, the authors refined airway structures within the local regions of FC with finer control. Cavity segmentation results were compared to the reference truths provided by expert radiologists and cavity formation was tracked longitudinally from serial CT scans through shape and volume information automatically determined through the authors' proposed system. Morphological evolution of the cavitary TB were analyzed accordingly with this process. Finally, the authors computed the minimum distance between cavity surface and nearby airway structures by using the linear time distance transform algorithm to explore potential role of airways in cavity formation and morphological evolution. Results: The proposed methodology was qualitatively and quantitatively evaluated on pulmonary CT images of rabbits experimentally infected with TB, and multiple markers such as cavity volume, cavity surface area, minimum distance from cavity surface to the nearest bronchial-tree, and longitudinal change of these markers (namely, morphological evolution of cavities) were determined precisely. While accuracy of the authors' cavity detection algorithm was 94.61%, airway detection part of the proposed methodology showed even higher performance by 99.8%. Dice similarity coefficients for cavitary segmentation experiments were found to be approximately 99.0% with respect to the reference truths provided by two expert radiologists (blinded to their evaluations). Moreover, the authors noted that volume derived from the authors' segmentation method was highly correlated with those provided by the expert radiologists (R 2 = 0.99757 and R 2 = 0.99496, p < 0.001, with respect to the observer 1 and observer 2) with an interobserver agreement of 98%. The authors quantitatively confirmed that cavity formation was positioned by the nearby bronchial-tree after exploring the respective spatial positions based on the minimum distance measurement. In terms of efficiency, the core algorithms take less than 2 min on a linux machine with 3.47 GHz CPU and 24 GB memory. Conclusion:The authors presented a fully automatic method for cavitary TB de...

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

“…Those segmentation methods have been shown to be effective in the calculation of lung volume and the initiation of computer-aided detection systems (10), which is considered in a wide range of clinical applications (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). However, those segmentation methods fail to perform efficiently when a pathologic condition or abnormality is present in moderate to marked lung volumes or demonstrates complex patterns of attenuation (11)(12)(13)(16)(17)(18).…”

Efficient ribcage segmentation from CT scans using shape features

“…For human image, slice thickness ranges from 1 mm to 5 mm (200 of them have thickness 5 mm, 90 of them have thickness 2.5 mm and the rest have thickness < 2 mm), while in-plane resolution ranges from 0.5 · 0.5 mm to 0.8 · 0.8 mm. For small animal images (rabbits and ferrets), the spatial resolution range from 0.2 · 0.2 mm to 0.3 · 0.3 mm in plane and 0.2 mm to 0.6 mm between slices ([9], [10]).…”

Accurate and efficient separation of left and right lungs from 3D CT scans: A generic hysteresis approach

“…Slice thickness ranges from 0.8 mm to 5 mm, while in-plane resolution ranges from 0.5 × 0.5 mm to 0.8 × 0.8 mm. For small animal images (rabbits and ferrets), the spatial resolution range from 0.2 × 0.2 mm to 0.3 × 0.3 mm in plane and 0.2 mm to 0.6 mm between slices ([10], [12]).…”

“…In this section, we first briefly introduce the initial lung segmentation method based on the theory of fuzzy connectedness (FC) [9] image segmentation along with trachea detection [10]. Subsequently, plate-like structure enhancement is explained following the Hessian filtering [11].…”

“…Specifically, adaptive threshold and connected component analysis is first utilized to extract shape features of the regions of interest. Then, a supervised machine learning method of support vector machine is applied to detect trachea [10] with accuracy of more than 99%. After detection, lung and trachea regions are extracted using FC with the detected seed points.…”