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The diverse shapes and scales, complicated backgrounds, blurred boundaries, and similar appearances challenge the current organ segmentation methods in medical scene images. It is difficult to acquire satisfactory performance to directly extend the object segmentation methods in the natural scene images to the medical scene images. In this paper, we propose a discriminant feature pyramid (DFPNet) network for organ segmentation in the original medical images, which consists of two sub-networks: the feature steered network and the border network. To be specific, the feature steered network takes a top-down step-wise manner to extract abundant context information, which is conducive to suppressing the cluttered background and perceiving the scale variation of objects. The border network utilizes a bottom-up step-wise manner to optimize the boundary feature map, which aims at distinguishing adjacent edge features with similar appearances but diverse labels. A series of experiments were conducted on three publicly available medical datasets (i.e., LUNA 16, RIM-ONE-R1, and VNC datasets) to evaluate the validity and generalization of the proposed DFPNet. Experimental results indicate that our network achieves superior performance in terms of the receiver operating characteristic (ROC) curve, F-Score, Jaccard index, and Hausdorff distance. The code will be available at: https://github.com/Xie-Xiwang/DFPNet. [ABSTRACT FROM AUTHOR]

Owing to the various object types and scales, complicated backgrounds, and similar appearance between tissues in medical images, it is difficult to extract some valuable information from different medical images. In this paper, we propose a context aware network with dual-stream pyramid (CANet) for medical image segmentation, which comprises a dual-stream pyramid module and an encoder–decoder module with context aware. Concretely, the dual-stream pyramid captures numerous complementary features at different layers by adopting multi-resolution input versions and multi-scale convolutional units, which is conductive to learning the local detail features in various scales. The encoder–decoder module with context aware progressively concatenates semantic features of the encoder branch with high-level features of the decoder branch in an efficient manner, which aims to suppress complicated backgrounds and highlight the most attractive object in medical images. Quantitative and qualitative experiments demonstrate that our CANet favorably performs against 13 state-of-the-art object segmentation methods on three publicly available medical image segmentation datasets. The code will be released at: https://github.com/Xie-Xiwang/BSPC2022_CANet. • We propose a context aware network with dual-stream pyramid (CANet) for medical image segmentation, which comprises a dual-stream pyramid module (L-shaped module) and an encoder–decoder network with context aware module (U-shaped module). • A novel dual-stream pyramid strategy is designed by multi-resolution input versions and multi-scale convolutional units. Compared with existing medical image segmentation methods, our method avoids the loss of local detail from the raw image and considers object scale variability. Furthermore, our method establishes the complementary relationship between local detail features and multi-scale features. • An encoder–decoder network with context aware is proposed by introducing a chain residual pooling module, which effectively suppresses complex background and provides discriminative feature representation for the decoding branch. [ABSTRACT FROM AUTHOR]

Arising from low contrast, high similarity, and different scales between diverse tissues in 2D medical images, it is challenging to accurately segment the regions of interest. To address these issues, we propose a context hierarchical integrated network named CHI-Net for medical image segmentation, which can accurately segment salient regions from medical images in a purely task-driven manner. The proposed CHI-Net consists of two key modules, i.e., a dense dilated convolution (DDC) and a stacked residual pooling (SRP). Specifically, The DDC module can capture substantial complementary features hierarchically by combining four cascaded branches of hybrid dilated convolutions, which is conducive to extracting the features of diverse scales. The SRP module integrates encoder detail features by multiple effective field-of-views, which aims at generating more discriminative features. Extensive experimental results on five benchmark datasets with different objects illustrate that the proposed CHI-Net is superior to the state-of-the-art object segmentation methods. [ABSTRACT FROM AUTHOR]