Automatic segmentation of levator hiatus from ultrasound images using U-net with dense connections.

Automatic segmentation of levator hiatus from ultrasound images using U-net with dense connections. Phys Med Biol. 2019 Mar 12;: Authors: Li X, Hong Y, Kong D, Zhang X Abstract In this paper, we propose a fully automatic method based on densely connected convolutional
 network for the segmentation of the levator hiatus from ultrasound images. A
 densely connected path is incorporated into a U-net to achieve a deep architecture and
 improve the segmentation performance. The proposed network architecture provides
 dense connections between layers that encourage feature reuse and reduce the number
 of parameters while maintaining good performance. The parameters of the network
 are optimized by training with a binary cross entropy i.e. logarithmic loss function. A
 dataset with 1000 levator hiatus images is used for training and 130 images are used for
 evaluating the performance of the proposed network architecture. The proposed model
 can get a mean Dice of 96.4% ± 0.7%. Experimental results show that the proposed
 method can achieve more accurate segmentation results. PMID: 30861502 [PubMed - as supplied by publisher]
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research