Discriminative features pyramid network for medical image segmentation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationMedical Image SegmentationOrgan SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Discriminative features pyramid network for medical image segmentation
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 perfor…
Image SegmentationMedical Image SegmentationOrgan SegmentationSegmentation+1CANet: Context aware network with dual-stream pyramid for medical image segmentation
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…
2D Semantic SegmentationDecoderImage SegmentationMedical Image Segmentation+3Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
Purpose: Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation …
Computed Tomography (CT)Image SegmentationLiver SegmentationMedical Image Segmentation+3Boundary-aware Context Neural Network for Medical Image Segmentation
Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. The performance of medical image segmentation has been significantly advanced with the convolutional neural net…
Image SegmentationMedical Image SegmentationMulti-Task LearningSegmentation+1DeepPyramid+: Medical Image Segmentation using Pyramid View Fusion and Deformable Pyramid Reception
Semantic Segmentation plays a pivotal role in many applications related to medical image and video analysis. However, designing a neural network architecture for medical image and surgical video segmentation is challengi…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+2