Abstract:In response to the growing importance of geospatial data, its analysis including semantic segmentation becomes an increasingly popular task in computer vision today. Convolutional neural networks are powerful visual models that yield hierarchies of features and practitioners widely use them to process remote sensing data. When performing remote sensing image segmentation, multiple instances of one class with precisely defined boundaries are often the case, and it is crucial to extract those boundaries accurate… Show more
“…Wu et al regularized the region-based CE loss based on the boundary loss for extracting building segments and outlines [9]. Bokhovkin et al proposed a surrogate loss to penalize the misalignment of building boundaries and achieved the out-performance than the commonly utilized CE and Dice losses [42]. Although the above-mentioned methods have significantly improved the performance of the building footprint generation, few work have been investigated to learn the segmentation models based on limited RS images with annotations.…”
Section: A Building Footprint Segmentationmentioning
“…Wu et al regularized the region-based CE loss based on the boundary loss for extracting building segments and outlines [9]. Bokhovkin et al proposed a surrogate loss to penalize the misalignment of building boundaries and achieved the out-performance than the commonly utilized CE and Dice losses [42]. Although the above-mentioned methods have significantly improved the performance of the building footprint generation, few work have been investigated to learn the segmentation models based on limited RS images with annotations.…”
Section: A Building Footprint Segmentationmentioning
“…Because of low contrast around the boundary of pancreas and tumor, exact determination of tumor and healthy tissue boundary can be challenging. To diminish this issue, we also use the differentiable version of the boundary loss proposed in (41). The boundaries of the ground truth and predicted segments are obtained using max-pooling operation as follow: where a pixel-wise max-pooling operation is applied to obtain the inverted ground truth and predicted binary segments with a kernel size θ 0 .…”
Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of Pancreatic Ductal Adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a new loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fin-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods (from 53.08% to 60.6%) in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.
“…Considering the imbalance of the number of edge pixels and non-edge pixels in the image, we draw inspiration from the boundary loss [39] and use the following function as the loss function for edge detection:…”
In this work, we propose a new deep convolution neural network (DCNN) architecture for semantic segmentation of aerial imagery. Taking advantage of recent research, we use split-attention networks (ResNeSt) as the backbone for high-quality feature expression. Additionally, a disentangled nonlocal (DNL) block is integrated into our pipeline to express the inter-pixel long-distance dependence and highlight the edge pixels simultaneously. Moreover, the depth-wise separable convolution and atrous spatial pyramid pooling (ASPP) modules are combined to extract and fuse multiscale contextual features. Finally, an auxiliary edge detection task is designed to provide edge constraints for semantic segmentation. Evaluation of algorithms is conducted on two benchmarks provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Extensive experiments demonstrate the effectiveness of each module of our architecture. Precision evaluation based on the Potsdam benchmark shows that the proposed DCNN achieves competitive performance over the state-of-the-art methods.
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