Selective Information Passing for MR/CT Image Segmentation

TL;DR

This paper introduces SIP-Net, a 3D medical image segmentation model that adaptively selects useful features for improved accuracy, outperforming existing methods across multiple datasets.

Contribution

The paper proposes a novel selective information passing network with self-supervised functions for adaptive feature selection in 3D medical image segmentation.

Findings

Achieved improved segmentation results on multiple datasets.

Outperformed state-of-the-art methods.

Demonstrated effectiveness of adaptive feature selection.

Abstract

Automated medical image segmentation plays an important role in many clinical applications, which however is a very challenging task, due to complex background texture, lack of clear boundary and significant shape and texture variation between images. Many researchers proposed an encoder-decoder architecture with skip connections to combine low-level feature maps from the encoder path with high-level feature maps from the decoder path for automatically segmenting medical images. The skip connections have been shown to be effective in recovering fine-grained details of the target objects and may facilitate the gradient back-propagation. However, not all the feature maps transmitted by those connections contribute positively to the network performance. In this paper, to adaptively select useful information to pass through those skip connections, we propose a novel 3D network with self-supervised function, named selective information passing network (SIP-Net). We evaluate our proposed model on the MICCAI Prostate MR Image Segmentation 2012 Grant Challenge dataset, TCIA Pancreas CT-82 and MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge dataset. The experimental results across these data sets show that our model achieved improved segmentation results and outperformed other state-of-the-art methods. The source code of this work is available at https://github.com/ahukui/SIPNet.