Segmentation of breast molybdenum target image lesions based on semi-supervised fuzzy clustering

Peng, Peng; Wu, Danping; Han, Fei-Chi; Huang, Lijun; Wei, Zhenlin; Wang, Jie; Jiang, Yizhang; Xia, Kaijian

doi:10.3233/jifs-224458

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Early image segmentation techniques, such as statistical shape [1][2], level set [3][4], fuzzy clustering [5][6]. Each approach has its own unique set of parameters that can be fine-tuned to meet the specific needs of different medical image scenarios.…”

Section: Introductionmentioning

confidence: 99%

MFI-Net: Multi-Level Feature Integration Network With SE-Res2Conv Encoder for Jaw Cyst Segmentation

Zheng,

Jiang,

et al. 2024

IEEE Access

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Accurate segmentation of organs and lesions from medical images holds paramount importance in aiding physicians with diagnosis and monitor diseases. At present, the widespread application of deeplearning in medical image segmentation is primarily attributed to its exceptional feature extraction capability. Nonetheless, due to blurred target boundary, wide range of changes and chaotic background, the segmentation of medical images is still faced with great challenges. To address these issues, we present a multi-level feature integration network (MFI-Net) with SE-Res2Conv encoder for jaw cyst segmentation. Specifically, we replace the original convolution operation with SE-Res2Conv to better maintain model's capacity for extracting features across multiple scales. Then, a novel context extractor module including multi-scale pooling block (MPB) and position attention module (PAM), which aims to generate more discriminative features. Finally, a multi-level feature integration block (MFIB) is implemented within the decoder to efficiently integrate low-level detail features with high-level semantic features. Numerous experiments were conducted on both the original and augmented datasets of jaw cyst to demonstrate the advantages of MFI-Net, with results consistently superior to all competitors. The Dice, IoU and Jaccard values of our method reached 93.06%, 93.47%, 87.06% in the original database and 91.25%, 91.94%, 84.06% in the augmented database. Furthermore, the computational efficiency of MFI-Net is impressive, with a speed of 106 FPS at the input size of 3×256×256 on a NVIDIA RTX6000 graphics card.

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Section: Introductionmentioning

confidence: 99%

MFI-Net: Multi-Level Feature Integration Network With SE-Res2Conv Encoder for Jaw Cyst Segmentation

Zheng,

Jiang,

et al. 2024

IEEE Access

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Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation

Peng

Huang

et al. 2023

Interdiscip Sci Comput Life Sci

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PalScDiff: A diffusion-based framework with progressive augmentation learning and semantic consistency for breast ultrasound tumor segmentation

Yang,

Tong

2024

IFS

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Background: Breast cancer diagnosis relies on accurate lesion segmentation in medical images. Automated computer-aided diagnosis reduces clinician workload and improves efficiency, but existing image segmentation methods face challenges in model performance and generalization. Objective: This study aims to develop a generative framework using a denoising diffusion model for efficient and accurate breast cancer lesion segmentation in medical images. Methods: We design a novel generative framework, PalScDiff, that leverages a denoising diffusion probabilistic model to reconstruct the label distribution for medical images, thereby enabling the sampling of diverse, plausible segmentation outcomes. Specifically, with the condition of the corresponding image, PalScDiff learns to estimate the masses region probability through denoising step by step. Furthermore, we design a Progressive Augmentation Learning strategy to incrementally handle segmentation challenges of irregular and blurred tumors. Moreover, multi-round sampling is employed to achieve robust breast mass segmentation. Results: Our experimental results show that PalScDiff outperforms established models such as U-Net and transformer-based alternatives, achieving an accuracy of 95.15%, precision of 79.74%, Dice coefficient of 77.61%, and Intersection over Union (IOU) of 81.51% . Conclusion: The proposed model demonstrates promising capabilities for accurate and efficient computer-aided segmentation of breast cancer.

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Segmentation of breast molybdenum target image lesions based on semi-supervised fuzzy clustering

Cited by 3 publications

References 34 publications

MFI-Net: Multi-Level Feature Integration Network With SE-Res2Conv Encoder for Jaw Cyst Segmentation

MFI-Net: Multi-Level Feature Integration Network With SE-Res2Conv Encoder for Jaw Cyst Segmentation

Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation

PalScDiff: A diffusion-based framework with progressive augmentation learning and semantic consistency for breast ultrasound tumor segmentation

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