Towards Fine Whole-Slide Skeletal Muscle Image Segmentation through Deep Hierarchically Connected Networks

Abstract EN

Automatic skeletal muscle image segmentation (MIS) is crucial in the diagnosis of muscle-related diseases.

However, accurate methods often suffer from expensive computations, which are not scalable to large-scale, whole-slide muscle images.

In this paper, we present a fast and accurate method to enable the more clinically meaningful whole-slide MIS.

Leveraging on recently popular convolutional neural network (CNN), we train our network in an end-to-end manner so as to directly perform pixelwise classification.

Our deep network is comprised of the encoder and decoder modules.

The encoder module captures rich and hierarchical representations through a series of convolutional and max-pooling layers.

Then, the multiple decoders utilize multilevel representations to perform multiscale predictions.

The multiscale predictions are then combined together to generate a more robust dense segmentation as the network output.

The decoder modules have independent loss function, which are jointly trained with a weighted loss function to address fine-grained pixelwise prediction.

We also propose a two-stage transfer learning strategy to effectively train such deep network.

Sufficient experiments on a challenging muscle image dataset demonstrate the significantly improved efficiency and accuracy of our method compared with recent state of the arts.