用于乳房 X 线照片肿块分割的预分割级联框架

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
International Journal of Biomedical Imaging Pub Date : 2024-08-09 eCollection Date: 2024-01-01 DOI:10.1155/2024/9422083
Urvi Oza, Bakul Gohel, Pankaj Kumar, Parita Oza
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引用次数: 0

摘要

准确分割乳房 X 光图像中的乳房肿块对早期癌症诊断和治疗计划至关重要。目前已提出了几种深度学习(DL)模型,用于整个乳房X光照片分割和肿块斑块/作物分割。然而,目前用于乳房X光照片肿块分割的深度学习模型面临着一些局限性,包括假阳性(FP)、假阴性(FN)以及端到端方法的挑战。本文提出了一种新颖的两阶段端到端级联乳腺肿块分割框架,该框架结合了潜在肿块区域的显著性地图来指导乳腺肿块分割的 DL 模型。级联框架的第一阶段分割模型用于生成显著性地图,以建立粗略的兴趣区域(ROI),从而有效地将焦点缩小到可能的肿块区域。在第二阶段的分割模型中引入了建议的前分区注意(PSA)块,以便根据生成的显著性地图动态适应乳房 X 光图像中信息量最大的区域。在骰子分数、精确度、召回率、FP 率 (FPR) 和 FN 结果方面,对有无级联框架的注意力 U 网模型进行了比较分析。实验结果一致表明,所提出的级联框架在所有三个数据集上都提高了乳房肿块的分割性能:INbreast、CSAW-S 和 DMID。级联框架显示出卓越的分割性能,在 INbreast 数据集上,骰子得分提高了约 6%,在 CSAW-S 数据集上提高了 3%,在 DMID 数据集上提高了 2%。同样,INbreast 数据集的 FN 结果降低了 10%,CSAW-S 数据集降低了 19%,DMID 数据集降低了 4%。此外,DeepLabV3+ 和 Swin transformer U-net 等各种最先进的分割模型也验证了所提出的级联框架的性能。无论选择哪种模型,预分割级联框架与任何医学图像分割框架集成后,都有可能提高分割性能并减少 FN。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Presegmenter Cascaded Framework for Mammogram Mass Segmentation.

Accurate segmentation of breast masses in mammogram images is essential for early cancer diagnosis and treatment planning. Several deep learning (DL) models have been proposed for whole mammogram segmentation and mass patch/crop segmentation. However, current DL models for breast mammogram mass segmentation face several limitations, including false positives (FPs), false negatives (FNs), and challenges with the end-to-end approach. This paper presents a novel two-stage end-to-end cascaded breast mass segmentation framework that incorporates a saliency map of potential mass regions to guide the DL models for breast mass segmentation. The first-stage segmentation model of the cascade framework is used to generate a saliency map to establish a coarse region of interest (ROI), effectively narrowing the focus to probable mass regions. The proposed presegmenter attention (PSA) blocks are introduced in the second-stage segmentation model to enable dynamic adaptation to the most informative regions within the mammogram images based on the generated saliency map. Comparative analysis of the Attention U-net model with and without the cascade framework is provided in terms of dice scores, precision, recall, FP rates (FPRs), and FN outcomes. Experimental results consistently demonstrate enhanced breast mass segmentation performance by the proposed cascade framework across all three datasets: INbreast, CSAW-S, and DMID. The cascade framework shows superior segmentation performance by improving the dice score by about 6% for the INbreast dataset, 3% for the CSAW-S dataset, and 2% for the DMID dataset. Similarly, the FN outcomes were reduced by 10% for the INbreast dataset, 19% for the CSAW-S dataset, and 4% for the DMID dataset. Moreover, the proposed cascade framework's performance is validated with varying state-of-the-art segmentation models such as DeepLabV3+ and Swin transformer U-net. The presegmenter cascade framework has the potential to improve segmentation performance and mitigate FNs when integrated with any medical image segmentation framework, irrespective of the choice of the model.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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