Vessel connectivity using Murray's hypothesis

Reconnection of Interrupted Curvilinear Structures via Cortically Inspired Completion for Ophthalmologic Images

OBJECTIVE

In this paper, we propose a robust, efficient, and automatic reconnection algorithm for bridging interrupted curvilinear skeletons in ophthalmologic images.

METHODS

This method employs the contour completion process, i.e., mathematical modeling of the direction process in the roto-translation group to achieve line propagation/completion. The completion process can be used to reconstruct interrupted curves by considering their local consistency. An explicit scheme with finite-difference approximation is used to construct the three-dimensional (3-D) completion kernel, where we choose the Gamma distribution for time integration. To process structures in , the orientation score framework is exploited to lift the 2-D curvilinear segments into the 3-D space. The propagation and reconnection of interrupted segments are achieved by convolving the completion kernel with orientation scores via iterative group convolutions. To overcome the problem of incorrect skeletonization of 2-D structures at junctions, a 3-D segment-wise thinning technique is proposed to process each segment separately in orientation scores.

RESULTS

Validations on 4 datasets with different image modalities show that our method achieves an average success rate of in reconnecting gaps of sizes from to , including challenging junction structures.

CONCLUSION

The reconnection approach can be a useful and reliable technique for bridging complex curvilinear interruptions.

SIGNIFICANCE

The presented method is a critical work to obtain more complete curvilinear structures in ophthalmologic images. It provides better topological and geometric connectivities for further analysis.

Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery

INTRODUCTION

Twin-to-twin transfusion syndrome (TTTS) is a potentially lethal condition that affects pregnancies in which twins share a single placenta. The definitive treatment for TTTS is fetoscopic laser photocoagulation, a procedure in which placental blood vessels are selectively cauterized. Challenges in this procedure include difficulty in quickly identifying placental blood vessels due to the many artifacts in the endoscopic video that the surgeon uses for navigation. We propose using deep-learned segmentations of blood vessels to create masks that can be recombined with the original fetoscopic video frame in such a way that the location of placental blood vessels is discernable at a glance.

METHODS

In a process approved by an institutional review board, intraoperative videos were acquired from ten fetoscopic laser photocoagulation surgeries performed at Yale New Haven Hospital. A total of 345 video frames were selected from these videos at regularly spaced time intervals. The video frames were segmented once by an expert human rater (a clinician) and once by a novice, but trained human rater (an undergraduate student). The segmentations were used to train a fully convolutional neural network of 25 layers.

RESULTS

The neural network was able to produce segmentations with a high similarity to ground truth segmentations produced by an expert human rater (sensitivity = 92.15% ± 10.69%) and produced segmentations that were significantly more accurate than those produced by a novice human rater (sensitivity = 56.87% ± 21.64%; p < 0.01).

CONCLUSION

A convolutional neural network can be trained to segment placental blood vessels with near-human accuracy and can exceed the accuracy of novice human raters. Recombining these segmentations with the original fetoscopic video frames can produced enhanced frames in which blood vessels are easily detectable. This has significant implications for aiding fetoscopic surgeons-especially trainees who are not yet at an expert level.