Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation

Lin, Yi; Zhang, Dong; Xiao, Fang; Chen, Yufan; Cheng, Kwang-Ting; Chen, Hao

doi:10.1007/978-3-031-34048-2_56

Cited by 22 publications

(2 citation statements)

References 31 publications

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“…The first-order derivative operators include Roberts, Prewitt, and Sobel, while the second-order derivative operators include Laplacian [84]. In recent years, boundary detection operators have regained importance in pixel-level computer vision tasks, such as camouflage object detection [3], [85], manipulation detection [86], and MISEG [12], gaining wide applications and research interests. Within this paper, we utilize edge detection operators to construct the AFCD as an explicit mask extractor.…”

Section: Operators In Image Processingmentioning

confidence: 99%

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Adaptive Dual-Stream Sparse Transformer Network for Salient Object Detection in Optical Remote Sensing Images

Zhao,

Jia,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Excellent performance has been demonstrated by convolutional neural network (CNN) in salient object detection for optical remote sensing images (ORSI-SOD). However, the limitations of CNN's feature extraction using sliding window approach hinder the capture of global representations. Therefore, an end-to-end detection model, known as adaptive dual-stream sparse transformer network (ADSTNet), has been proposed for ORSI-SOD and is assisted by the vision transformer. It effectively addresses the compensation issue of global and local information in ORSI-SOD. In particular, an adaptive interaction encoder has been devised, amalgamating the multi-scale sparse transformer (MST) and the pyramid atrous attention (PAA) to constitute the adaptive dual-stream sparse encoder (ADSE). This encoder collaborates with the CNN to enhance longrange dependency modeling and preserve global information more effectively base on local features. Additionally, a directional feature reconfiguration (DFR) is constructed to extract texture details from multiple directional dimensions. Finally, we propose the adaptive feature cascade decoder (AFCD) that synthesizes content information from the foreground, edges, and background to enhance the representational capacity of the image. Furthermore, a structural loss function, known as the weight compensation mechanism, is introduced to balance the performance of boundary and salmap segmentation losses. The proposed model has been demonstrated to outperform 26 stateof-the-art (SOTA) ORSI-SOD methods across eight evaluation metrics on two standard datasets, as evidenced by extensive experiments. Furthermore, to verify its robustness, the generalization performance of the model on the latest challenging ORSI-4199 dataset is reported. The code and results for this work can be found at https://github.com/JieZzzoo/ADSTNet.

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Section: Operators In Image Processingmentioning

confidence: 99%

“…detection [3], [4], human-computer interaction [5]- [8], video surveillance [9]- [11], and medical imaging [12]- [15]. Our focus is on salient object detection for optical remote sensing images (ORSI-SOD) [16]- [19], distinguishing it from classical salient object detection in natural scene images (NSI-SOD) [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Dual-Stream Sparse Transformer Network for Salient Object Detection in Optical Remote Sensing Images

Zhao,

Jia,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation

Ding,

Liu,

et al. 2024

Advanced Intelligent Systems

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To solve the problems of existing hybrid networks based on convolutional neural networks (CNN) and Transformers, we propose a new encoder–decoder network FI‐Net based on CNN‐Transformer for medical image segmentation. In the encoder part, a dual‐stream encoder is used to capture local details and long‐range dependencies. Moreover, the attentional feature fusion module is used to perform interactive feature fusion of dual‐branch features, maximizing the retention of local details and global semantic information in medical images. At the same time, the multi‐scale feature aggregation module is used to aggregate local information and capture multi‐scale context to mine more semantic details. The multi‐level feature bridging module is used in skip connections to bridge multi‐level features and mask information to assist multi‐scale feature interaction. Experimental results on seven public medical image datasets fully demonstrate the effectiveness and advancement of our method. In future work, we plan to extend FI‐Net to support 3D medical image segmentation tasks and combine self‐supervised learning and knowledge distillation to alleviate the overfitting problem of limited data training.

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Pan‐cancer image segmentation based on feature pyramids and Mask R‐CNN framework

Wang,

Zhou,

Wang

2024

Medical Physics

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BackgroundCancer, a disease with a high mortality rate, poses a great threat to patients' physical and mental health and can lead to huge medical costs and emotional damage. With the continuous development of artificial intelligence technologies, deep learning‐based cancer image segmentation techniques are becoming increasingly important in cancer detection and accurate diagnosis. However, in segmentation tasks, there are differences in efficiency between large and small objects and limited segmentation effects on objects of individual sizes. The previous segmentation frameworks still have room for improvement in multi‐scale collaboration when segmenting objects.PurposeThis paper proposes a method to train a deep learning segmentation framework using a feature pyramid processing dataset to improve the average precision (AP) index, and realizes multi‐scale cooperation in target segmentation.ObjectivePan‐Cancer Histology Dataset for Nuclei Instance Segmentation and Classification (PanNuke) dataset was selected to include approximately 7500 pathology images with cells from 19 different types of tissues, including five classifications of cancer, non‐cancer, inflammation, death, and connective tissue.MethodsFirst, the method uses whole‐slide images in the pan‐cancer histology dataset for nuclei instance segmentation and classification (PanNuke) dataset, combined with the mask region convolutional neural network (Mask R‐CNN) segmentation framework and improved loss function to segment and detect each cellular tissue in cancerous sections. Second, to address the problem of non‐synergistic object segmentation at different scales in cancerous tissue segmentation, a scheme using feature pyramids to process the dataset was adopted as part of the feature extraction module.ResultsExtensive experimental results on this dataset show that the method in this paper yields 0.269 AP and a boost of about 4% compared to the original Mask R‐CNN framework.ConclusionsIt is effective and feasible to use feature pyramid to process data set to improve the effect of medical image segmentation.

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Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation

Cited by 22 publications

References 31 publications

Adaptive Dual-Stream Sparse Transformer Network for Salient Object Detection in Optical Remote Sensing Images

Adaptive Dual-Stream Sparse Transformer Network for Salient Object Detection in Optical Remote Sensing Images

FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation

Pan‐cancer image segmentation based on feature pyramids and Mask R‐CNN framework

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