Enhancing Crack Segmentation Network with Multiple Selective Fusion Mechanisms by Yang Chen, Tao Yang, Shuai Dong, Like Wang, Bida Pei, Yunlong Wang

“…The results demonstrate that the proposed segmentation network achieves superior performance in pixel-level crack segmentation, with <i>Dice</i> scores of 66.2%, 54.2%, and 86.8% and <i>mIoU</i> values of 74.4%, 67.5%, and 87.9% on the SCD, CFD, and DeepCrack datasets, respectively. …”
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Segmentation and particle size analysis of coal particles based on ISUNet by Deqiang CHENG, Rui ZHANG, Tongxi XIE, Jingjing LIU, Lijuan ZHENG, Qiqi KOU, He JIANG

“…In the process of digital image segmentation, global information and edge details play a crucial role and directly affect the accuracy of the segmentation results. …”
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High-Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise by Haiping Jiang, Salah Bourennane, Caroline Fossati

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Management of Sternal Segment Dislocation in a Child with Closed Reduction by Omer Soysal, Osman Cemil Akdemir, Sedat Ziyade, Murat Ugurlucan

“…Dislocation of a sternal segment in the childhood period is very rare as for sternal fractures in children. …”
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Stereo Image Segmentation Based on Graph Cut and Visual Correlation by Qingyan Dai, Zhongjie Zhu

“…Stereo image segmentation is a crucial and difficult issue in the field of object-based stereo image processing.By improving the Grabcut algorithm and exploiting the inter-view correlations,a novel stereo image segmentation scheme was proposed.The original left image was firstly transformed into a super-pixel image by improving the Slic algorithm.Then the super-pixel image was segmented and the foreground object in the left image is extracted based on the framework of Grabcut,where the energy function was rebuilt.Finally,by exploiting the inter-view correlations between the left and the right images,the foreground object in the right image was obtained based on contour correspondence by fusing color and texture features.The experimental results show that the proposed algorithm is more efficient and can achieve better segmentation results than the existing methods.…”
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Stereo Image Segmentation Based on Graph Cut and Visual Correlation by Qingyan Dai, Zhongjie Zhu

“…Stereo image segmentation is a crucial and difficult issue in the field of object-based stereo image processing.By improving the Grabcut algorithm and exploiting the inter-view correlations,a novel stereo image segmentation scheme was proposed.The original left image was firstly transformed into a super-pixel image by improving the Slic algorithm.Then the super-pixel image was segmented and the foreground object in the left image is extracted based on the framework of Grabcut,where the energy function was rebuilt.Finally,by exploiting the inter-view correlations between the left and the right images,the foreground object in the right image was obtained based on contour correspondence by fusing color and texture features.The experimental results show that the proposed algorithm is more efficient and can achieve better segmentation results than the existing methods.…”
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Application of Semi-Supervised Mean Teacher to Rock Image Segmentation by Jiashan Li, Yuxue Wang

“…To address the issue of requiring a large number of labeled images for model training in traditional image segmentation methods, this paper proposes an improved semi-supervised Mean Teacher algorithm based on ResNet34-UNet. …”
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Exploring the spatial segmentation of housing markets from online listings by David Abella, Johann H. Martínez, Mattia Mazzoli, Julien Migozzi, Thibault Le Corre, Eduard Alonso-Paulí, Rafel Crespí-Cladera, Thomas Louail, José J. Ramasco

“…This methodology addresses the long-standing issue of housing market segmentation, relevant in disciplines such as urban studies and spatial economics, and with implications for policymaking.…”
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Segmentation study of nanoparticle topological structures based on synthetic data. by Fengfeng Liang, Yu Zhang, Chuntian Zhou, Heng Zhang, Guangjie Liu, Jinlong Zhu

“…However, in materials science, the acquisition of training images requires a large number of professionals and the labor cost is extremely high, so there are usually very few training samples in the field of materials. In this study, a segmentation method of nanoparticle topological structure based on synthetic data (SD) is proposed, which aims to solve the issue of small data in the field of materials. …”
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BEHAVIORAL SEGMENTATION OF CUSTOMERS: RFM MODEL AND K-MEANS APPLICATION by Thanh L.T.P., Liaw S.-Y., Hien N.T.

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Medical Images Segmentation Based on Unsupervised Algorithms: A Review by Revella E. A. Armya, Adnan Mohsin Abdulazeez

“…To better address, this issue, a variety of measurement standards have been suggested to decide the consistency of the segmentation outcome. …”
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Evaluation of synthetic data impact on fire segmentation models performance by Matej Arlovic, Franko Hrzic, Mitesh Patel, Tomasz Bednarz, Josip Balen

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A brain tumor segmentation method based on attention mechanism by Juan Cao, Jinjia Liu, Jiaran Chen

“…Additionally, a spatial attention module (SPA) is introduced to establish global feature correlations, critical for accurate tumor segmentation. To address class imbalance, which can hinder performance, we propose BADLoss, a loss function tailored to mitigate this issue. …”
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Efficient Segmentation Using Attention-Fusion Modules With Dense Predictions by Serdar Erisen

“…This approach also enhances the performance of state-of-the-art segmentation networks, addressing the challenges issued by foundation models like InternImage. …”
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A weakly-supervised follicle segmentation method in ultrasound images by Guanyu Liu, Weihong Huang, Yanping Li, Qiong Zhang, Jing Fu, Hongying Tang, Jia Huang, Zhongteng Zhang, Lei Zhang, Yu Wang, Jianzhong Hu

“…By leveraging Multiple Instance Learning (MIL), we formulated the learning process in a weakly supervised manner and developed an end-to-end trainable model that efficiently addresses the issue of annotation scarcity. Furthermore, the WSFS can be used as a prompt proposal to enhance the performance of the Segmentation Anything Model (SAM), a well-known pre-trained segmentation model utilizing few-shot learning strategies. …”
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Interactive image segmentation algorithm based on adaptive kernel learning by LONG Jianwu, LI Jihao

“…To address the issue that most existing interactive image segmentation methods suffer from limited segmentation performance due to their susceptibility to noise interference and non-convex structure impacts in the original feature space, an adaptive kernel learning-based interactive image segmentation algorithm was proposed. …”
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An Improved Small Target Segmentation Model Based on Mask Dino by Jun Yang, Xu Chen, Yun Guan, Yixuan Hu, Gang Ge

“…To address the issue of low segmentation accuracy for small objects in the Mask Dino segmentation method, we propose an improved small object segmentation model called FFMask Dino. …”
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Confident Learning-Based Label Correction for Retinal Image Segmentation by Tanatorn Pethmunee, Supaporn Kansomkeat, Patama Bhurayanontachai, Sathit Intajag

“…<b>Background/Objectives:</b> In automatic medical image analysis, particularly for diabetic retinopathy, the accuracy of labeled data is crucial, as label noise can significantly complicate the analysis and lead to diagnostic errors. To tackle the issue of label noise in retinal image segmentation, an innovative label correction framework is introduced that combines Confident Learning (CL) with a human-in-the-loop re-annotation process to meticulously detect and rectify pixel-level labeling inaccuracies. …”
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Intracorneal Ring Segments in Keratoconus: A Narrative Literature Review by Hayat UK, Shiwani HA, Memon D, Walkden A

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Update on recent hot issues and progress of acute kidney injury by Liang Xin-ling, Chen Yuan-han, Wu Yan-hua

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