Automatic segmentation of orbital wall from CT images via a thin wall region supervision-based multi-scale feature search network

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.

A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images

The human upper airway is comprised of many anatomical volumes. The obstructions in the upper airway volumes are needed to be diagnosed which requires volumetric segmentation. Manual segmentation is time-consuming and requires expertise in the field. Automatic segmentation provides reliable results and also saves time and effort for the expert. The objective of this study is to systematically review the literature to study various techniques used for the automatic segmentation of the human upper airway regions in volumetric images. PRISMA guidelines were followed to conduct the systematic review. Four online databases Scopus, Google Scholar, PubMed, and JURN were used for the searching of the relevant papers. The relevant papers were shortlisted using inclusion and exclusion eligibility criteria. Three review questions were made and explored to find their answers. The best technique among all the literature studies based on the Dice coefficient and precision was identified and justified through the analysis. This systematic review provides insight to the researchers so that they shall be able to overcome the prominent issues in the field identified from the literature. The outcome of the review is based on several parameters, e.g., accuracy, techniques, challenges, datasets, and segmentation of different sub-regions. Flowchart of the search process as per PRISMA guidelines along with inclusion and exclusion criteria.

Building a three-dimensional model of early-stage zebrafish embryo brain

We introduce a computational approach to build three-dimensional (3D) surface mesh models of the early-stage zebrafish brain primordia from time-series microscopy images. The complexity of the early-stage brain primordia and lack of recognizable landmarks pose a distinct challenge for feature segmentation and 3D modeling. Additional difficulty arises because of noise and variations in pixel intensity. We overcome these by using a hierarchical approach in which simple geometric elements, such as "beads" and "bonds," are assigned to represent local features and their connectivity is used to smoothen the surface while retaining high-curvature regions. We apply our method to build models of two zebrafish embryo phenotypes at discrete time points between 19 and 28 h post-fertilization and collect measurements to quantify development. Our approach is fast and applicable to building models of other biological systems, as demonstrated by models from magnetic resonance images of the human fetal brain. The source code, input scripts, sample image files, and generated outputs are publicly available on GitHub.

Artificial Intelligence and Big Data

Medical images play an important role in ophthalmology and radiology. Medical image analysis has greatly benefited from the application of "deep learning" techniques in clinical and experimental radiology. Clinical applications and their relevance for radiological imaging in ophthalmology are presented.

Three-dimensional orbital wall modeling using paranasal sinus segmentation

PURPOSE

Three-dimensional orbital wall modeling is a time-consuming process because of the presence of pseudoforamina. We developed an automated three-dimensional modeling software to characterize the orbital wall, and evaluated it using data from fracture patients.

METHODS

We first characterized the air and face regions using multiphase segmentation; the sinuses were segmented by applying morphological operations to air regions. Pseudoforamina of the orbital wall were offset with the segmented sinuses. Finally, the three-dimensional facial bone model, with orbital wall, was reconstructed from the segmented images.

RESULTS

Ten computed tomography data sets were used to evaluate the proposed method. Results were compared with those obtained using the active contour model and manual segmentation. The process took 31.7 ± 8.0 s, which was 30-60 times faster than other methods. The average distances between surfaces obtained with the proposed method and those obtained with manual segmentation (normal side: 0.20 ± 0.06 mm; fractured side: 0.28 ± 0.10 mm) were approximately half those obtained using the active contour model.

CONCLUSIONS

Three-dimensional orbital wall models, which were very similar to the manually segmented models, were archived within 1 min using the developed software, regardless of fracture presence. The proposed method might improve the safety and accuracy of surgical procedures.