B Pavihaa Lakshmi, S Vidhya
School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.
Sci Rep. 2024 Dec 28;14(1):30835. doi: 10.1038/s41598-024-81703-5.
A new era for diagnosing and treating Deep Vein Thrombosis (DVT) relies on precise segmentation from medical images. Our research introduces a novel algorithm, the Modified-Net architecture, which integrates a broad spectrum of architectural components tailored to detect the intricate patterns and variances in DVT imaging data. Our work integrates advanced components such as dilated convolutions for larger receptive fields, spatial pyramid pooling for context, residual and inception blocks for multiscale feature extraction, and attention mechanisms for highlighting key features. Our framework enhances precision of DVT region identification, attaining an accuracy of 98.92%, with a loss of 0.0269. The model also validates sensitivity 96.55%, specificity 96.70%, precision 98.61%, dice 97.48% and Intersection over Union (IoU) 95.10% offering valuable insights into DVT segmentation. Our framework significantly improves segmentation performance over traditional methods such as Convolutional Neural Network , Sequential, U-Net, Schematic. The management of DVT can be improved through enhanced segmentation techniques, which can improve clinical observation, treatment planning, and ultimately patient outcomes.
诊断和治疗深静脉血栓形成（DVT）的新时代依赖于医学图像的精确分割。我们的研究引入了一种新颖的算法——改进网络架构，它集成了广泛的架构组件，旨在检测DVT成像数据中的复杂模式和差异。我们的工作集成了先进的组件，如用于更大感受野的扩张卷积、用于上下文的空间金字塔池化、用于多尺度特征提取的残差块和inception块，以及用于突出关键特征的注意力机制。我们的框架提高了DVT区域识别的精度，准确率达到98.92%，损失为0.0269。该模型还验证了敏感性为96.55%、特异性为96.70%、精度为98.61%、骰子系数为97.48%以及交并比（IoU）为95.10%，为DVT分割提供了有价值的见解。我们的框架显著提高了分割性能，优于传统方法，如卷积神经网络、顺序模型、U-Net、示意图模型。通过增强分割技术可以改善DVT的管理，这可以改善临床观察、治疗计划，并最终改善患者的治疗结果。
