Random Region Matting for the High-Resolution PolSAR Image Semantic Segmentation

Ni, Jun; Zhang, Fan; Ma, Fei; Yin, Qiang; Xiang, Deliang

doi:10.1109/jstars.2021.3062447

Cited by 13 publications

(5 citation statements)

References 43 publications

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“…It has exhibited the most promising predictions and outcomes. The DeepLabV3+model, extensively utilized in various other studies and research articles, has found applications in diverse areas, ranging from automatic brain tumor segmentation 51 to Random Region Matting for High-Resolution PolSAR Image Semantic Segmentation 52 . Nonetheless, its implementation in this particular study targeting MS patients has shown unparalleled performance and the most favorable predictions, signifying a groundbreaking advancement in the field.…”

Section: Resultsmentioning

confidence: 99%

Efficient segmentation of active and inactive plaques in FLAIR-images using DeepLabV3Plus SE with efficientnetb0 backbone in multiple sclerosis

Naeeni Davarani,

Arian Darestani,

Guillen Cañas

et al. 2024

Sci Rep

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This research paper introduces an efficient approach for the segmentation of active and inactive plaques within Fluid-attenuated inversion recovery (FLAIR) images, employing a convolutional neural network (CNN) model known as DeepLabV3Plus SE with the EfficientNetB0 backbone in Multiple sclerosis (MS), and demonstrates its superior performance compared to other CNN architectures. The study encompasses various critical components, including dataset pre-processing techniques, the utilization of the Squeeze and Excitation Network (SE-Block), and the atrous spatial separable pyramid Block to enhance segmentation capabilities. Detailed descriptions of pre-processing procedures, such as removing the cranial bone segment, image resizing, and normalization, are provided. This study analyzed a cross-sectional cohort of 100 MS patients with active brain plaques, examining 5000 MRI slices. After filtering, 1500 slices were utilized for labeling and deep learning. The training process adopts the dice coefficient as the loss function and utilizes Adam optimization. The study evaluated the model's performance using multiple metrics, including intersection over union (IOU), Dice Score, Precision, Recall, and F1-Score, and offers a comparative analysis with other CNN architectures. Results demonstrate the superior segmentation ability of the proposed model, as evidenced by an IOU of 69.87, Dice Score of 76.24, Precision of 88.89, Recall of 73.52, and F1-Score of 80.47 for the DeepLabV3+SE_EfficientNetB0 model. This research contributes to the advancement of plaque segmentation in FLAIR images and offers a compelling approach with substantial potential for medical image analysis and diagnosis.

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Section: Resultsmentioning

confidence: 99%

Efficient segmentation of active and inactive plaques in FLAIR-images using DeepLabV3Plus SE with efficientnetb0 backbone in multiple sclerosis

Naeeni Davarani,

Arian Darestani,

Guillen Cañas

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…It has exhibited the most promising predictions and outcomes. The DeepLabv3 + model, extensively utilized in various other studies and research articles, has found applications in diverse areas, ranging from automatic brain tumor segmentation (43) to Random Region Matting for the High-Resolution PolSAR Image Semantic Segmentation (44). Nonetheless, its implementation in this particular study targeting MS patients has shown unparalleled performance and the most favorable predictions, signifying a groundbreaking advancement in the eld.…”

Section: Resultsmentioning

confidence: 99%

Efficient Segmentation of Active and Inactive Plaques in FLAIR-Images Using DeepLabV3Plus SE with EfficientNetB0 Backbone in Multiple Sclerosis

Davarani,

Darestani,

Cañas

et al. 2023

Preprint

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This research paper introduces an efficient approach for the segmentation of active and inactive plaques within FLAIR-images, employing a Convolutional Neural Network (CNN) model known as DeepLabV3Plus SE with the EfficientNetB0 backbone in Multiple sclerosis, and demonstrates its superior performance compared to other CNN architectures. The study encompasses various critical components, including dataset pre-processing techniques, the utilization of the Squeeze and Excitation Network (SE-Block), and the Atrous Spatial Separable Pyramid (ASPP) Block to enhance segmentation capabilities. Detailed descriptions of pre-processing procedures, such as removing the skull's bony portion, image resizing, and normalization, are provided. The training process adopts the dice coefficient as the loss function and utilizes Adam optimization. The study evaluated the model's performance using multiple metrics, including Intersection Over Union (IOU), Dice Score, Precision, Recall, and F1-Score, and offers a comparative analysis with other CNN architectures. Results demonstrate the superior segmentation ability of the proposed model, as evidenced by an IOU of 69.87, Dice Score of 76.24, Precision of 88.89, Recall of 73.52, and F1-Score of 80.47 for the DeepLabV3 + SE_EfficientNetB0 model. This research contributes to the advancement of plaque segmentation in FLAIR images and offers a compelling approach with substantial potential for medical image analysis and diagnosis.

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“…In target segmentation research, the articles [18][19][20][21][22][23] use edge information as a guide to sharpen the target, and edge information is mostly integrated from the contextual semantics to obtain the localization information to assist in the fusion of the synthesized high and low level feature information and thus achieve segmentation, whereas the article [23] obtains the edge semantics by using mask-guided pyramid networks; in target detection research, the articles [24,25] used the encoder part of different scales to progressively fuse features with the target significant edge extraction network to form a U-shaped structure to merge the object features and enhance the edges to cope with the rough boundaries of the object; However, most of the current research in the direction of target detection is aimed at the edges of significant targets, and it is still worthwhile to further improve and explore the structure of the guidance network for infrared ship images with low contrast and blurred contour boundaries.…”

Section: A Edge-guided Schemementioning

confidence: 99%

EGISD-YOLO: Edge Guidance Network for Infrared Ship Target Detection

Zhan,

Zhang,

Guo

et al. 2024

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

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In the marine field, infrared detection technology is of great significance for timely localization and detection of ships in security missions. However, since infrared ship targets are often in the environmental conditions of small pixel occupancy, low contrast and complex background, it poses a great challenge for multi-ship detection, classification and localization tasks. Therefore, to address these problems we propose an edge informationguided infrared ship target detection network (EGISD-YOLO), in which a dense-csp structure is designed to improve the csp module of YOLO to increase the reusability of the backbone feature information, and in addition, to address the noise and interference generated by the image in the complex background, a deconvolutional channel attention module (DCA) is designed to link the contextual language to the image. DCA), which relates the contextual semantics to obtain the local information of the target. Crucially, we propose an edge-guided structure that takes the edge information of low-level features as a cue to fuse with deep-level features to enrich the target contour and thus improve the target localization ability, so that the network still possesses robustness under low-contrast conditions, and finally, we add a small-size prediction head at the end of the network to further increase the detection ability of weak targets. The proposed EGISD-YOLO is experimentally demonstrated to have better detection performance for infrared ship targets.

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Random Region Matting for the High-Resolution PolSAR Image Semantic Segmentation

Cited by 13 publications

References 43 publications

Efficient segmentation of active and inactive plaques in FLAIR-images using DeepLabV3Plus SE with efficientnetb0 backbone in multiple sclerosis

Efficient segmentation of active and inactive plaques in FLAIR-images using DeepLabV3Plus SE with efficientnetb0 backbone in multiple sclerosis

Efficient Segmentation of Active and Inactive Plaques in FLAIR-Images Using DeepLabV3Plus SE with EfficientNetB0 Backbone in Multiple Sclerosis

EGISD-YOLO: Edge Guidance Network for Infrared Ship Target Detection

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