Computerized assessment of pulmonary fissure integrity using high resolution CT

Attention U-net for automated pulmonary fissure integrity analysis in lung computed tomography images

Computed Tomography (CT) imaging is routinely used for imaging of the lungs. Deep learning can effectively automate complex and laborious tasks in medical imaging. In this work, a deep learning technique is utilized to assess lobar fissure completeness (also known as fissure integrity) from pulmonary CT images. The human lungs are divided into five separate lobes, divided by the lobar fissures. Fissure integrity assessment is important to endobronchial valve treatment screening. Fissure integrity is known to be a biomarker of collateral ventilation between lobes impacting the efficacy of valves designed to block airflow to diseased lung regions. Fissure integrity is also likely to impact lobar sliding which has recently been shown to affect lung biomechanics. Further widescale study of fissure integrity's impact on disease susceptibility and progression requires rapid, reproducible, and noninvasive fissure integrity assessment. In this paper we describe IntegrityNet, an attention U-Net based automatic fissure integrity analysis tool. IntegrityNet is able to predict fissure integrity with an accuracy of 95.8%, 96.1%, and 89.8% for left oblique, right oblique, and right horizontal fissures, compared to manual analysis on a dataset of 82 subjects. We also show that our method is robust to COPD severity and reproducible across subject scans acquired at different time points.

The Emerging Role of Quantification of Imaging for Assessing the Severity and Disease Activity of Emphysema, Airway Disease, and Interstitial Lung Disease

There has been an explosion of use for quantitative image analysis in the setting of lung disease due to advances in acquisition protocols and postprocessing technology, including machine and deep learning. Despite the plethora of published papers, it is important to understand which approach has clinical validation and can be used in clinical practice. This paper provides an introduction to quantitative image analysis techniques being used in the investigation of lung disease and focusses on the techniques that have a reasonable clinical validation for being used in clinical trials and patient care.

An open-source framework for pulmonary fissure completeness assessment

We present an open-source framework for pulmonary fissure completeness assessment. Fissure incompleteness has been shown to associate with emphysema treatment outcomes, motivating the development of tools that facilitate completeness estimation. Generally, the task of fissure completeness assessment requires accurate detection of fissures and definition of the boundary surfaces separating the lung lobes. The framework we describe acknowledges a) the modular nature of fissure detection and lung lobe segmentation (lobe boundary detection), and b) that methods to address these challenges are varied and continually developing. It is designed to be readily deployable on existing lung lobe segmentation and fissure detection data sets. The framework consists of multiple components: a flexible quality control module that enables rapid assessment of lung lobe segmentations, an interactive lobe segmentation tool exposed through 3D Slicer for handling challenging cases, a flexible fissure representation using particles-based sampling that can handle fissure feature-strength or binary fissure detection images, and a module that performs fissure completeness estimation using voxel counting and a novel surface area estimation approach. We demonstrate the usage of the proposed framework by deploying on 100 cases exhibiting various levels of fissure completeness. We compare the two completeness level approaches and also compare to visual reads. The code is available to the community via github as part of the Chest Imaging Platform and a 3D Slicer extension module.

Preoperative delineation of pulmonary fissural anatomy at multi-detector computed tomography in children with congenital pulmonary malformations and impact on surgical complications and postoperative course

BACKGROUND

Delineation of the anatomy and integrity of the pulmonary fissures at CT is important because anomalous or incomplete fissures might increase the risk of surgery and of postoperative complications.

OBJECTIVE

To preoperatively evaluate the integrity of the pleural fissures in children with congenital lung malformations and determine whether anomalous fissural anatomy is a risk factor for a more complicated surgery and postoperative course.

MATERIALS AND METHODS

We reviewed preoperative multi-detector CT scans of consecutive children who underwent open or thoracoscopic resection of a congenital pulmonary malformation from 2008 to 2018, to determine the integrity of the fissural anatomy, and compared these findings with the surgical report. We correlated postoperative factors including operating room time, days in hospital and chest tube with the operating room documented fissural integrity.

RESULTS

We saw a significant association between the radiologically determined fissural integrity at CT and the operative findings independently for the right, left and both lungs combined (P<0.001). The sensitivity of CT to determine fissural integrity was 76.9%, specificity 95.2%, positive predictive value 95.2%, negative predictive value 76.9%, and accuracy 85.1%. There was a statistically significant association between size of the pulmonary malformation and the integrity of the fissure(s) (P=0.024). Larger lesions also resulted in a significantly longer hospitalization (P=0.024).

CONCLUSION

Chest CT showed high accuracy for delineating fissural anatomy in children with congenital pulmonary malformations, with a good interobserver correlation. Incomplete lung fissures were found more often in children with larger congenital pulmonary malformations. In addition, larger lesions were associated with longer hospital stays. Therefore, children with incomplete fissures may have a longer postoperative course. Analysis of the fissural anatomy should be included in the CT report.

Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter

Pulmonary fissures are important landmarks for recognition of lung anatomy. In CT images, automatic detection of fissures is complicated by factors like intensity variability, pathological deformation and imaging noise. To circumvent this problem, we propose a derivative of stick (DoS) filter for fissure enhancement and a post-processing pipeline for subsequent segmentation. Considering a typical thin curvilinear shape of fissure profiles inside 2D cross-sections, the DoS filter is presented by first defining nonlinear derivatives along a triple stick kernel in varying directions. Then, to accommodate pathological abnormality and orientational deviation, a [Formula: see text] cascading and multiple plane integration scheme is adopted to form a shape-tuned likelihood for 3D surface patches discrimination. During the post-processing stage, our main contribution is to isolate the fissure patches from adhering clutters by introducing a branch-point removal algorithm, and a multi-threshold merging framework is employed to compensate for local intensity inhomogeneity. The performance of our method was validated in experiments with two clinical CT data sets including 55 publicly available LOLA11 scans as well as separate left and right lung images from 23 GLUCOLD scans of COPD patients. Compared with manually delineating interlobar boundary references, our method obtained a high segmentation accuracy with median F1-scores of 0.833, 0.885, and 0.856 for the LOLA11, left and right lung images respectively, whereas the corresponding indices for a conventional Wiemker filtering method were 0.687, 0.853, and 0.841. The good performance of our proposed method was also verified by visual inspection and demonstration on abnormal and pathological cases, where typical deformations were robustly detected together with normal fissures.

Correlation between collateral ventilation and interlobar lung fissures

BACKGROUND

New bronchoscopic techniques for end-stage emphysema treatment are nowadays available; the presence of interlobar collateral ventilation (CV) and interlobar lung fissures (ILF) is crucial for patient selection.

OBJECTIVES

Assessment of these variables has been reported previously, but it has never been anatomically validated in vivo. This is the purpose of our study.

METHODS

Twenty-one patients undergoing lung resection for lung cancer were prospectively enrolled in this study. At operation, CV was assessed by the Chartis catheter system. ILF completeness at high-resolution computed tomography (HRCT) was retrospectively reviewed. The ILF status at HRCT and at surgery was compared; furthermore, the relationship between CV and ILF status was assessed.

RESULTS

At HRCT, ILF were incomplete in 18 cases; at catheter evaluation, CV was present in 11 cases; 15 patients had incomplete ILF at operation. HRCT specificity, sensitivity and accuracy were 33, 93 and 76% compared with ILF status at surgery. HRCT accuracy was 90% on the right and 63% on the left. We demonstrated a high grade of probability of CV presence and incomplete ILF at surgery (odds ratio = 10.0).

CONCLUSIONS

There is a correlation between ILF status and CV. Both catheter evaluation of CV and HRCT assessment of ILF show some limitations. However, the cumulative information provided by these techniques allows to reliably assess the anatomical ILF status.

Automatic pulmonary fissure detection and lobe segmentation in CT chest images

BACKGROUND

Multi-detector Computed Tomography has become an invaluable tool for the diagnosis of chronic respiratory diseases. Based on CT images, the automatic algorithm to detect the fissures and divide the lung into five lobes will help regionally quantify, amongst others, the lung density, texture, airway and, blood vessel structures, ventilation and perfusion.

METHODS

Sagittal adaptive fissure scanning based on the sparseness of the vessels and bronchi is employed to localize the potential fissure region. Following a Hessian matrix based line enhancement filter in the coronal slice, the shortest path is determined by means of Uniform Cost Search. Implicit surface fitting based on Radial Basis Functions is used to extract the fissure surface for lobe segmentation. By three implicit fissure surface functions, the lung is divided into five lobes. The proposed algorithm is tested by 14 datasets. The accuracy is evaluated by the mean (±S.D.), root mean square, and the maximum of the shortest Euclidian distance from the manually-defined fissure surface to that extracted by the algorithm.

RESULTS

Averaged over all datasets, the mean (±S.D.), root mean square, and the maximum of the shortest Euclidian distance are 2.05 ± 1.80, 2.46 and 7.34 mm for the right oblique fissure. The measures are 2.77 ± 2.12, 3.13 and 7.75 mm for the right horizontal fissure, 2.31 ± 1.76, 3.25 and 6.83 mm for the left oblique fissure. The fissure detection works for the data with a small lung nodule nearby the fissure and a small lung subpleural nodule. The volume and emphysema index of each lobe can be calculated. The algorithm is very fast, e.g., to finish the fissure detection and fissure extension for the dataset with 320 slices only takes around 50 seconds.

CONCLUSIONS

The sagittal adaptive fissure scanning can localize the potential fissure regions quickly. After the potential region is enhanced by a Hessian based line enhancement filter, Uniform Cost Search can extract the fissures successfully in 2D. Surface fitting is able to obtain three implicit surface functions for each dataset. The current algorithm shows good accuracy, robustness and speed, may help locate the lesions into each lobe and analyze them regionally.

Pulmonary fissure integrity and collateral ventilation in COPD patients

Purpose

To investigate whether the integrity (completeness) of pulmonary fissures affects pulmonary function in patients with chronic obstructive pulmonary disease (COPD).

Materials and Methods

A dataset consisting of 573 CT exams acquired on different subjects was collected from a COPD study. According to the global initiative for chronic obstructive lung disease (GOLD) criteria, these subjects (examinations) were classified into five different subgroups, namely non-COPD (222 subjects), GOLD-I (83 subjects), GOLD-II (141 subjects), GOLD-III (63 subjects), and GOLD-IV (64 subjects), in terms of disease severity. An available computer tool was used to aid in an objective and efficient quantification of fissure integrity. The correlations between fissure integrity, and pulmonary functions (e.g., FEV1, and FEV1/FVC) and COPD severity were assessed using Pearson and Spearman's correlation coefficients, respectively.

Results

For the five sub-groups ranging from non-COPD to GOLD-IV, the average integrities of the right oblique fissure (ROF) were 81.8%, 82.4%, 81.8%, 82.8%, and 80.2%, respectively; the average integrities of the right horizontal fissure (RHF) were 62.6%, 61.8%, 62.1%, 62.2%, and 62.3%, respectively; the average integrities of the left oblique fissure (LOF) were 82.0%, 83.2%, 81.7%, 82.0%, and 78.4%, respectively; and the average integrities of all fissures in the entire lung were 78.0%, 78.6%, 78.1%, 78.5%, and 76.4%, respectively. Their Pearson correlation coefficients with FEV1 and FE1/FVC range from 0.027 to 0.248 with p values larger than 0.05. Their Spearman correlation coefficients with COPD severity except GOLD-IV range from −0.013 to −0.073 with p values larger than 0.08.

Conclusion

There is no significant difference in fissure integrity for patients with different levels of disease severity, suggesting that the development of COPD does not change the completeness of pulmonary fissures and incomplete fissures alone may not contribute to the collateral ventilation.

Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review

Computed tomography (CT) is the modality of choice for imaging the lungs in vivo. Sub-millimeter isotropic images of the lungs can be obtained within seconds, allowing the detection of small lesions and detailed analysis of disease processes. The high resolution of thoracic CT and the high prevalence of lung diseases require a high degree of automation in the analysis pipeline. The automated segmentation of pulmonary structures in thoracic CT has been an important research topic for over a decade now. This systematic review provides an overview of current literature. We discuss segmentation methods for the lungs, the pulmonary vasculature, the airways, including airway tree construction and airway wall segmentation, the fissures, the lobes and the pulmonary segments. For each topic, the current state of the art is summarized, and topics for future research are identified.

Incomplete pulmonary fissures evaluated by volumetric thin-section CT: semi-quantitative evaluation for small fissure gaps identification, description of prevalence and severity of fissural defects

OBJECTIVE

To assess the interobserver agreement for a semi-quantitative evaluation of the interlobar fissures integrity in volumetric thin-section CT images, looking for more detailed information regarding fissural defects; and describe prevalence and severity of fissural defects between the different functional groups of subjects.

MATERIALS AND METHODS

Volumetric scans of 247 individuals exposed to tobacco with different functional status (normal to severe COPD), were retrospectively and independently evaluated by 2 chest radiologists, with a consensual reading additionally with a third reader in disagreement cases. Right oblique (RO), right horizontal (RH) and left oblique fissures (LO) integrity was estimated using a 5% scale. GOLD classification was available for all subjects.

RESULTS

Interobserver agreement (weighted Kappa-index) for fissural categorization was 0.76, 0.70 and 0.75, for RO, RH and LO, respectively. Final evaluation found 81%, 89% and 50% of RO, RH and LO to be incomplete, with respective mean integrity of 80%, 58% and 80%. Small fissure gaps (<10%) were present in 30% of patients. Prevalence and severity of fissural defects were not different between the GOLD categories.

CONCLUSIONS

A substantial agreement between readers was found in the analysis of interlobar fissures integrity. The semi-quantitative method allowed a detailed description of the fissural defects, information that can be important, for example, in endoscopic lung volume reduction therapies for emphysema. Small fissure gaps, overlooked in previous studies, were found in almost a third of the patients. A higher than previously described prevalence of fissural defects was described, but without significant differences among the distinct functional groups.

Identification of pulmonary fissures using a piecewise plane fitting algorithm

We describe an automated computerized scheme to identify pulmonary fissures depicted in chest computed tomography (CT) examinations from a novel perspective. Whereas CT images can be regarded as a cloud of points, the underlying idea is to search for surface-like structures in the three-dimensional (3D) Euclidean space by using an efficient plane fitting algorithm. The proposed plane fitting operation is performed in a number of small spherical lung sub-volumes to detect small planar patches. Using a simple clustering criterion based on their spatial coherence and surface area, the identified planar patches, assumed to represent fissures, are classified into different types of fissures, namely left oblique, right oblique and right horizontal fissures. The performance of the developed scheme was assessed by comparing with a manually created "reference standard" and the results obtained by a previously developed approach on a dataset of 30 lung CT examinations. The experiments show that the average discrepancy is around 1.0mm in comparison with the reference standard, while the corresponding maximum discrepancy is 20.5mm. In addition, 94% of the fissure voxels identified by the computerized scheme are within 3mm of the fissures in the reference standard. As compared to a previously developed approach, we also found that the newly developed scheme had a smaller discrepancy with the standard reference. In efficiency, it takes approximately 8 min to identify the fissures in a chest CT examination on a typical PC. The developed scheme demonstrates a reasonable performance in terms of accuracy, robustness, and computational efficiency.