A Novel Tool for Supervised Segmentation Using 3D Slicer

Creation of Three-dimensional Anatomic Models in Pediatric Surgical Patients Using Cross-sectional Imaging: A Demonstration of Low-cost Methods and Applications

BACKGROUND

Pediatric surgery patients often present with complex congenital anomalies or other conditions requiring deep understanding of their intricate anatomy. Commercial applications and services exist for the conversion of cross-sectional imaging data into three-dimensional (3D) models for education and preoperative planning. However, the associated costs and lack of familiarity may discourage their use in centers with limited resources. The purpose of this report is to present a low-cost, reproducible method for generating 3D images to visualize patient anatomy.

METHODS

De-identified DICOM files were obtained from the hospital PACS system in preparation for assorted pediatric surgical procedures. Using open-source visualization software, variations in anatomic structures were examined using volume rendering and segmentation techniques. Images were further refined using available editing tools or artificial intelligence-assisted software extensions.

RESULTS

Using the described techniques we were able to obtain excellent visualization of desired structures and associated anatomic variations. Once structures were selected and modeled in 3D (segmentation), they could be exported as one of several 3D object file formats. These could then be retained for 3D printing, visualization in virtual reality, or as an anatomic reference during the perioperative period. Models may also be imported into commercial gaming engines for rendering under optimal lighting conditions and with enhanced detail.

CONCLUSION

Pediatric surgeons are frequently tasked with the treatment of patients with complex and rare anomalies. Visualization and preoperative planning can be assisted by advanced imaging software at minimal to no cost, thereby facilitating enhanced understanding of these conditions in resource-limited environments.

LEVEL OF EVIDENCE

V, Case Series, Description of Technique.

A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images

Glioma is a relatively common brain tumor disease with high mortality rate. Humans have been seeking a more effective therapy. In the course of treatment, the specific location of the tumor needs to be determined first in any case. Therefore, how to segment tumors from brain tissue accurately and quickly is a persistent problem. In this paper, a new dual-stream decoding CNN architecture combined with U-net for automatic segmentation of brain tumor on MR images namely DDU-net is proposed. Two edge-based optimization strategies are used to enhance the performance of brain tumor segmentation. First, we design a separate branch to process edge stream information. Here, high level edge features are reduced in dimension of channel and integrated into the conventional semantic stream in the way of residual. Second, a regularization loss function is used to encourage the predicted segmentation mask to align with ground truth around the edge mainly by penalizing pixels where the predicted segmentation masks and labels do not match around the edge. In training, we employ a novel edge extraction algorithm for providing edge labels with higher quality. Moreover, we add a self-adaptive balancing class weight coefficient into the cross entropy loss function for solving the serious class imbalance problem in the backpropagation of edge extraction. Our experiments show that this leads to a very efficient architecture which can produce clearer prediction at the edge of the tumor. Our method achieves ideal performance on BraTS2017 and BraTS2018 in terms of Dice coefficient.

Symmetry in Engineering Sciences

The symmetry concept is mainly used in two senses. The first from the aesthetic point of view of proportionality or harmony, since human beings seek symmetry in nature. Or the second, from an engineering point of view to attend to geometric regularities or to explain a repetition process or pattern in a given phenomenon. This special issue dedicated to geometry in engineering deals with this last concept, which aims to collect both the aspects of geometric solutions in engineering, which may even have a certain aesthetic character, and the aspect of the use of patterns that explain observed phenomena.