Iterative Deep Convolutional Encoder-Decoder Network for Medical Image Segmentation

TL;DR

This paper introduces an iterative deep convolutional encoder-decoder network that enhances medical image segmentation accuracy by progressively refining ROI localization, outperforming existing methods.

Contribution

It presents a novel iterative learning framework combining encoder-decoder architecture for improved medical image segmentation.

Findings

Achieved superior segmentation accuracy compared to state-of-the-art methods.

Effectively localizes complex shapes and detailed textures in medical images.

Demonstrated robustness across various medical imaging datasets.

Abstract

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.