Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation.

Purpose: Our study investigates the potential benefits of incorporating prior anatomical knowledge into a deep learning (DL) method designed for the automated segmentation of lung lobes in chest CT scans. Approach: We introduce an automated DL-based approach that leverages anatomical information from the lung's vascular system to guide and enhance the segmentation process. This involves utilizing a lung vessel connectivity (LVC) map, which encodes relevant lung vessel anatomical data. Our study explores the performance of three different neural network architectures within the nnU-Net framework: a standalone U-Net, a multitasking U-Net, and a cascade U-Net. Results: Experimental findings suggest that the inclusion of LVC information in the DL model can lead to improved segmentation accuracy, particularly, in the challenging boundary regions of expiration chest CT volumes. Furthermore, our study demonstrates the potential for LVC to enhance the model's generalization capabilities. Finally, the method's robustness is evaluated through the segmentation of lung lobes in 10 cases of COVID-19, demonstrating its applicability in the presence of pulmonary diseases. Conclusions: Incorporating prior anatomical information, such as LVC, into the DL model shows promise for enhancing segmentation performance, particularly in the boundary regions. However, the extent of this improvement has limitations, prompting further exploration of its practical applicability.

Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction.

OBJECTIVES: Quantitative CT imaging is an important emphysema biomarker, especially in smoking cohorts, but does not always correlate to radiologists' visual CT assessments. The objectives were to develop and validate a neural network-based slice-wise whole-lung emphysema score (SWES) for chest CT, to validate SWES on unseen CT data, and to compare SWES with a conventional quantitative CT method. MATERIALS AND METHODS: Separate cohorts were used for algorithm development and validation. For validation, thin-slice CT stacks from 474 participants in the prospective cross-sectional Swedish CArdioPulmonary bioImage Study (SCAPIS) were included, 395 randomly selected and 79 from an emphysema cohort. Spirometry (FEV1/FVC) and radiologists' visual emphysema scores (sum-visual) obtained at inclusion in SCAPIS were used as reference tests. SWES was compared with a commercially available quantitative emphysema scoring method (LAV950) using Pearson's correlation coefficients and receiver operating characteristics (ROC) analysis. RESULTS: SWES correlated more strongly with the visual scores than LAV950 (r = 0.78 vs. r = 0.41, p < 0.001). The area under the ROC curve for the prediction of airway obstruction was larger for SWES than for LAV950 (0.76 vs. 0.61, p = 0.007). SWES correlated more strongly with FEV1/FVC than either LAV950 or sum-visual in the full cohort (r = - 0.69 vs. r = - 0.49/r = - 0.64, p < 0.001/p = 0.007), in the emphysema cohort (r = - 0.77 vs. r = - 0.69/r = - 0.65, p = 0.03/p = 0.002), and in the random sample (r = - 0.39 vs. r = - 0.26/r = - 0.25, p = 0.001/p = 0.007). CONCLUSION: The slice-wise whole-lung emphysema score (SWES) correlates better than LAV950 with radiologists' visual emphysema scores and correlates better with airway obstruction than do LAV950 and radiologists' visual scores. CLINICAL RELEVANCE STATEMENT: The slice-wise whole-lung emphysema score provides quantitative emphysema information for CT imaging that avoids the disadvantages of threshold-based scores and is correlated more strongly with reference tests than LAV950 and reader visual scores. KEY POINTS: • A slice-wise whole-lung emphysema score (SWES) was developed to quantify emphysema in chest CT images. • SWES identified visual emphysema and spirometric airflow limitation significantly better than threshold-based score (LAV950). • SWES improved emphysema quantification in CT images, which is especially useful in large-scale research.

Automatic estimation of the cross-sectional area of the waist of the nerve fibre layer at the optic nerve head.

PURPOSE: Glaucoma leads to pathological loss of axons in the retinal nerve fibre layer at the optic nerve head (ONH). This study aimed to develop a strategy for the estimation of the cross-sectional area of the axons in the ONH. Furthermore, improving the estimation of the thickness of the nerve fibre layer, as compared to a method previously published by us. METHODS: In the 3D-OCT image of the ONH, the central limit of the pigment epithelium and the inner limit of the retina, respectively, were identified with deep learning algorithms. The minimal distance was estimated at equidistant angles around the circumference of the ONH. The cross-sectional area was estimated by the computational algorithm. The computational algorithm was applied on 16 non-glaucomatous subjects. RESULTS: The mean cross-sectional area of the waist of the nerve fibre layer in the ONH was 1.97 ± 0.19 mm2 . The mean difference in minimal thickness of the waist of the nerve fibre layer between our previous and the current strategies was estimated as CIµ (0.95) 0 ± 1 µm (d.f. = 15). CONCLUSIONS: The developed algorithm demonstrated an undulating cross-sectional area of the nerve fibre layer at the ONH. Compared to studies using radial scans, our algorithm resulted in slightly higher values for cross-sectional area, taking the undulations of the nerve fibre layer at the ONH into account. The new algorithm for estimation of the thickness of the waist of the nerve fibre layer in the ONH yielded estimates of the same order as our previous algorithm.