3D visualization and segmentation are increasingly widely used in physical, biological and medical science, facilitating advanced investigative methodologies. However, the integration and reproduction of segmented volumes or results across the spectrum of mainstream 3D visualization platforms remain hindered by compatibility constraints. These barriers not only challenge the replication of findings but also obstruct the process of cross-validating the accuracy of 3D visualization outputs. To address this gap, we developed an innovative revisualization method implemented within the open-source framework of Drishti, a 3D visualization software. Leveraging four animal samples alongside three mainstream 3D visualization platforms as case studies, our method demonstrates the seamless transferability of segmented results into Drishti. This capability effectively fosters a new avenue for authentication and enhanced scrutiny of segmented data. By facilitating this interoperability, our approach underscores the potential for significant advancements in accuracy validation and collaborative research efforts across diverse scientific domains.

BACKGROUND: Allergic rhinitis constitutes a widespread health concern, with traditional treatments often proving to be painful and ineffective. Acupuncture targeting the pterygopalatine fossa proves effective but is complicated due to the intricate nearby anatomy.
METHODS: To enhance the safety and precision in targeting the pterygopalatine fossa, we introduce a deep learning-based model to refine the segmentation of the pterygopalatine fossa. Our model expands the U-Net framework with DenseASPP and integrates an attention mechanism for enhanced precision in the localisation and segmentation of the pterygopalatine fossa.
RESULTS: The model achieves Dice Similarity Coefficient of 93.89% and 95% Hausdorff Distance of 2.53 mm with significant precision. Remarkably, it only uses 1.98 M parameters.
CONCLUSIONS: Our deep learning approach yields significant advancements in localising and segmenting the pterygopalatine fossa, providing a reliable basis for guiding pterygopalatine fossa-assisted punctures.

Intervertebral disc herniation, degenerative lumbar spinal stenosis, and other lumbar spine diseases can occur across most age groups. MRI examination is the most commonly used detection method for lumbar spine lesions with its good soft tissue image resolution. However, the diagnosis accuracy is highly dependent on the experience of the diagnostician, leading to subjective errors caused by diagnosticians or differences in diagnostic criteria for multi-center studies in different hospitals, and inefficient diagnosis. These factors necessitate the standardized interpretation and automated classification of lumbar spine MRI to achieve objective consistency. In this research, a deep learning network based on SAFNet is proposed to solve the above challenges.
In this research, low-level features, mid-level features, and high-level features of spine MRI are extracted. ASPP is used to process the high-level features. The multi-scale feature fusion method is used to increase the scene perception ability of the low-level features and mid-level features. The high-level features are further processed using global adaptive pooling and Sigmoid function to obtain new high-level features. The processed high-level features are then point-multiplied with the mid-level features and low-level features to obtain new high-level features. The new high-level features, low-level features, and mid-level features are all sampled to the same size and concatenated in the channel dimension to output the final result.
The DSC of SAFNet for segmenting 17 vertebral structures among 5 folds are 79.46 ± 4.63%, 78.82 ± 7.97%, 81.32 ± 3.45%, 80.56 ± 5.47%, and 80.83 ± 3.48%, with an average DSC of 80.32 ± 5.00%. The average DSC was 80.32 ± 5.00%. Compared to existing methods, our SAFNet provides better segmentation results and has important implications for the diagnosis of spinal and lumbar diseases.
This research proposes SAFNet, a highly accurate and robust spine segmentation deep learning network capable of providing effective anatomical segmentation for diagnostic purposes. The results demonstrate the effectiveness of the proposed method and its potential for improving radiological diagnosis accuracy.

UNASSIGNED: Lymphovascular invasion (LVI) is an invasive biologic behavior that affects the treatment and prognosis of patients with early-stage lung cancer. This study aimed to identify LVI diagnostic and prognostic biomarkers using deep learning-powered 3D segmentation with artificial intelligence (AI) technology.
UNASSIGNED: Between January 2016 and October 2021, we enrolled patients with clinical T1 stage non-small cell lung cancer (NSCLC). We used commercially available AI software (Dr. Wise system, Deep-wise Corporation, China) to extract quantitative AI features of pulmonary nodules automatically. Dimensionality reduction was achieved through least absolute shrinkage and selection operator regression; subsequently, the AI score was calculated.Then, the univariate and multivariate analysis was further performed on the AI score and patient baseline parameters.
UNASSIGNED: Among 175 enrolled patients, 22 tested positive for LVI at pathology review. Based on the multivariate logistic regression results, we incorporated the AI score, carcinoembryonic antigen, spiculation, and pleural indentation into the nomogram for predicting LVI. The nomogram showed good discrimination (C-index = 0.915 [95% confidence interval: 0.89-0.94]); moreover, calibration of the nomogram revealed good predictive ability (Brier score = 0.072). Kaplan-Meier analysis revealed that relapse-free survival and overall survival were significantly higher among patients with a low-risk AI score and without LVI than those among patients with a high-risk AI score (p = 0.008 and p = 0.002, respectively) and with LVI (p = 0.013 and p = 0.008, respectively).
UNASSIGNED: Our findings indicate that a high-risk AI score is a diagnostic biomarker for LVI in patients with clinical T1 stage NSCLC; accordingly, it can serve as a prognostic biomarker for these patients.

The vessel-like structure in biomedical images, such as within cerebrovascular and nervous pathologies, is an essential biomarker in understanding diseases\' mechanisms and in diagnosing and treating diseases. However, existing vessel-like structure segmentation methods often produce unsatisfactory results due to challenging segmentations for crisp edges. The edge and nonedge voxels of the vessel-like structure in three-dimensional (3D) medical images usually have a highly imbalanced distribution as most voxels are non-edge, making it challenging to find crisp edges. In this work, we propose a generic neural network for the segmentation of the vessel-like structures in different 3D medical imaging modalities. The new edge-reinforced neural network (ER-Net) is based on an encoder-decoder architecture. Moreover, a reverse edge attention module and an edge-reinforced optimization loss are proposed to increase the weight of the voxels on the edge of the given 3D volume to discover and better preserve the spatial edge information. A feature selection module is further introduced to select discriminative features adaptively from an encoder and decoder simultaneously, which aims to increase the weight of edge voxels, thus significantly improving the segmentation performance. The proposed method is thoroughly validated using four publicly accessible datasets, and the experimental results demonstrate that the proposed method generally outperforms other state-of-the-art algorithms for various metrics.

We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes; (ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations; (iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++ able to aggregate rich detailed anatomical structure of the brain; (iv) our Dual-PRNet++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet++ outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations.

The uniqueness and reliability of frontal sinuses for personal identification have gained wide recognition in forensics. However, few studies have assessed the usefulness of a three-dimensional (3D) model of the frontal sinus for human identification. This study aimed to develop standardized techniques to classify the frontal sinus according to its 3D morphological metrics and discover the usefulness of the 3D frontal sinus model in identification of Chinese Han population. One hundred and ninety-six computed tomography (CT) scans of patients older than 20 years (84 males and 112 females) were collected. A 3D frontal sinus digital model was segmented using Dolphin Imaging software. The following morphological metrics of the 3D frontal sinus were used to develop the coding system: bilateral or unilateral, spatial relationships of the two sides, number of septations, superior volume side, the shape of the 3D model of each side, shape of the medial surface and frontal ostium on each side, number of accessory septations on each side, number of supra-orbital cells of the medial surface and lateral surface on each side, and number of the arcades on each side. The new coding system accurately identified all of our research individuals. This study discovered a number of individual variations in the 3D frontal sinus morphology patterns. A coding system, which is based on these morphological patterns, exposes the morphological variants of frontal sinuses and presents the usefulness of 3D frontal sinus model for human identification.