IBA-U-Net: Attentive BConvLSTM U-Net with Redesigned Inception for medical image segmentation

Abstract

Accurate segmentation of medical images plays an essential role in their analysis and has a wide range of research and application values in fields of practice such as medical research, disease diagnosis, disease analysis, and auxiliary surgery. In recent years, deep convolutional neural networks have been developed that show strong performance in medical image segmentation. However, because of the inherent challenges of medical images, such as irregularities of the dataset and the existence of outliers, segmentation approaches have not demonstrated sufficiently accurate and reliable results for clinical employment. Our method is based on three key ideas: (1) integrating the BConvLSTM block and the Attention block to reduce the semantic gap between the encoder and decoder feature maps to make the two feature maps more homogeneous, (2) factorizing convolutions with a large filter size by Redesigned Inception, which uses a multiscale feature fusion method to significantly increase the effective receptive field, and (3) devising a deep convolutional neural network with multiscale feature fusion and a Attentive BConvLSTM mechanism, which integrates the Attentive BConvLSTM block and the Redesigned Inception block into an encoder-decoder model called Attentive BConvLSTM U-Net with Redesigned Inception (IBA-U-Net). Our proposed architecture, IBA-U-Net, has been compared with the U-Net and state-of-the-art segmentation methods on three publicly available datasets, the lung image segmentation dataset, skin lesion image dataset, and retinal blood vessel image segmentation dataset, each with their unique challenges, and it has improved the prediction performance even with slightly less calculation expense and fewer network parameters. By devising a deep convolutional neural network with a multiscale feature fusion and Attentive BConvLSTM mechanism, medical image segmentation of different tasks can be completed effectively and accurately with only 45% of U-Net parameters.