The current investigation intended to evaluate quantities that express the minimal cross section of the waist of the nerve fiber layer, to develop age adjusted tolerance intervals for not pathological, develop a strategy for sensitive longitudinal monitoring of glaucoma within an individual eye, to develop an intuitive patient record illustrating the monitored eye, and finally to apply the strategy for longitudinal monitoring of glaucoma on a prospective longitudinal cohort. Volumes of the optical nerve head were captured with the Topcon Triton system. Raw data were exported to a custom made deep learning system for segmentation of the minimal cross section of the waist of the nerve fiber layer in the optic nerve head (ONH). Then, the Pigment epithelium central limit-Inner limit of the retina–Minimal Distance (PIMD, two strategies), or Minimal Area (PIMA) was estimated angularly resolved in the frontal plane. Age adjusted reference levels for not pathological eye were derived from clinically not pathological eyes. Reference levels for progression were derived from the early to manifest glaucoma eye measured. Tolerance intervals for patient visit 1-3 were developed based on variability estimated in a not pathological population. Tolerance intervals for visit 4 were developed based on variability within the examined eye. There was insignificant difference between the two strategies for PIMD estimation. PIMA-angle should be limited to resolution in clock hrs. The developed patient record efficiently illustrated the relationship between estimate and progression with tolerance limit in early to manifest glaucoma eyes and a case of traumatic optical atrophy.

Glaucoma, a leading cause of blindness, results in progressive vision loss if untreated. Optical coherence tomography (OCT) enables the measurement of retinal nerve fiber layers and the optic nerve head (ONH). Považay et al. introduced the Pigment epithelium central limit-Inner limit of the retina Minimal Distance averaged over 2π radians (PIMD-2π) to quantify the minimal cross-section of the nerve fiber l ayer i n the ONH. The present research enhances automated PIMD estimation in OCT images, employing the nnU-Net model for semantic segmentation. Using a dataset of 78 OCT images from Uppsala University, experiments were conducted in cylindrical (2D U-Net and nnU-Net) and Cartesian domains (nnU-Net). Results show that the nnU-Net frameworks significantly improve OPCL coordinate accuracy (mean Euclidean distance in pixel value: 1.665 for cylindrical and 2.4495 for Cartesian) compared to 2D U-Net (10.6827). Notably, the nnU-Net Cartesian architecture removes manual bias from ONH center selection during cylindrical transformations. PIMD calculations effectively distinguished glaucoma patients from healthy subjects, with nnU-Net methods demonstrating superior stability. This study underscores the potential of automated PIMD estimation in advancing glaucoma .

Maxillary Sinus Lifting is a crucial surgical procedure for addressing insufficient alveolar bone mass andsevere resorption in dental implant therapy. To accurately analyze the geometry changesof the bone graft (BG) in the maxillary sinus (MS), it is essential to perform quantitative analysis. However, automated BG segmentation remains a major challenge due to the complex local appearance, including blurred boundaries, lesion interference, implant and artifact interference, and BG exceeding the MS. Currently, there are few tools available that can efficiently and accurately segment BG from cone beam computed tomography (CBCT) image. In this paper, we propose a distance-constrained attention network guided by prior anatomical knowledge for the automatic segmentation of BG. First, a guidance strategy of preoperative prior anatomical knowledge is added to a deep neural network (DNN), which improves its ability to identify the dividing line between the MS and BG. Next, a coordinate attention gate is proposed, which utilizes the synergy of channel and position attention to highlight salient features from the skip connections. Additionally, the geodesic distance constraint is introduced into the DNN to form multi-task predictions, which reduces the deviation of the segmentation result. In the test experiment, the proposed DNN achieved a Dice similarity coefficient of 85.48 +/- 6.38%, an average surface distance error is 0.57 +/- 0.34mm, and a 95% Hausdorff distance of 2.64 +/- 2.09mm, which is superior to the comparison networks. It markedly improves the segmentation accuracy and efficiency of BG and has potential applications in analyzing its volume change and absorption rate in the future.

Vessel segmentation in 2D/3D images is crucial for accurate computer-assisted diagnosis and preoperative planning. However, due to noise, complex topology, and low contrast with the background, previous segmentation algorithms are prone to missing some segments of vessels and generating breakpoints in the segmentation maps, resulting in discontinuities in the extracted centerline. In this paper, to preserve the correct topology in the segmentation maps, we propose an innovative connectivity-preserving dice (coDice) loss function. coDice is calculated by comparing the local regions connected to a common seed point in both the predicted and ground-truth segmentation. Extending this, we also propose three types of seed generation strategies that can be used in conjunction with the proposed coDice loss. Preliminary experiments on both 2D and 3D images show that the proposed coDice loss can improve segmentation accuracy and region connectivity in tubular structure segmentation.

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.

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.

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). ConclusionT he 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 statementThe 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.

Purpose: Orbital wall segmentation is critical for orbital measurement and reconstruction. However, the orbital floor and medial wall are made up of thin walls (TW) with low gradient values, making it difficult to segment the blurred areas of the CT images. Clinically, doctors have to manually repair the missing parts of TW, which is time-consuming and laborious. Methods: To address these issues, this paper proposes an automatic orbital wall segmentation method based on TW region supervision using a multi-scale feature search network. First of all, in the encoding branch, the densely connected atrous spatial pyramid pooling based on the residual connection is adopted to achieve a multi-scale feature search. Then, for feature enhancement, multi-scale up-sampling and residual connection are applied to perform skip connection of features in multi-scale convolution. Finally, we explore a strategy for improving the loss function based on the TW region supervision, which effectively increases the TW region segmentation accuracy. Results: The test results show that the proposed network performs well in terms of automatic segmentation. For the whole orbital wall region, the Dice coefficient (Dice) of segmentation accuracy reaches 96.086 ± 1.049%, the Intersection over Union (IOU) reaches 92.486 ± 1.924%, and the 95% Hausdorff distance (HD) reaches 0.509 ± 0.166 mm. For the TW region, the Dice reaches 91.470 ± 1.739%, the IOU reaches 84.327 ± 2.938%, and the 95% HD reaches 0.481 ± 0.082 mm. Compared with other segmentation networks, the proposed network improves the segmentation accuracy while filling the missing parts in the TW region. Conclusion: In the proposed network, the average segmentation time of each orbital wall is only 4.05 s, obviously improving the segmentation efficiency of doctors. In the future, it may have a practical significance in clinical applications such as preoperative planning for orbital reconstruction, orbital modeling, orbital implant design, and so on.