Iterative Deep Convolutional Encoder-Decoder Network for Medical Image Segmentation
This addresses medical image segmentation for healthcare applications, representing an incremental improvement over existing methods.
The paper tackles medical image segmentation by proposing an iterative deep convolutional encoder-decoder network that combines iterative learning with an encoder-decoder architecture to precisely localize regions of interest, including complex shapes and textures. Experimental results show it yields excellent segmentation performance and outperforms state-of-the-art methods.
In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.