Sridhar Kalaivani, C Kavitha, Lai Wen-Cheng, Kavin Balasubramanian Prabhu
Department of Computer Science, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu, India.
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, Tamil Nadu, India.
Biomedicines. 2023 Mar 6;11(3):800. doi: 10.3390/biomedicines11030800.
Systems for medical analytics and decision making that make use of multimodal intelligence are of critical importance in the field of healthcare. Liver cancer is one of the most frequent types of cancer and early identification of it is crucial for effective therapy. Liver tumours share the same brightness and contrast characteristics as their surrounding tissues. Likewise, irregular tumour shapes are a serious concern that varies with cancer stage and tumour kind. There are two main phases of tumour segmentation in the liver: identifying the liver, and then segmenting the tumour itself. Conventional interactive segmentation approaches, however, necessitate a high number of intensity levels, whereas recently projected CNN-based interactive segmentation approaches are constrained by low presentation on liver tumour images. This research provides a unique deep Learning based Segmentation with Coot Extreme Learning Model approach that shows high efficiency in results and also detects tumours from the publicly available data of liver images. Specifically, the study processes the initial segmentation with a small number of additional users clicks to generate an improved segmentation by incorporating inner boundary points through the proposed geodesic distance encoding method. Finally, classification is carried out using an Extreme Learning Model, with the classifier's parameters having been ideally chosen by means of the Coot Optimization algorithm (COA). On the 3D-IRCADb1 dataset, the research evaluates the segmentation quality metrics DICE and accuracy, finding improvements over approaches in together liver-coloured and tumour separation.
利用多模态智能的医学分析和决策系统在医疗保健领域至关重要。肝癌是最常见的癌症类型之一，早期识别对有效治疗至关重要。肝肿瘤与其周围组织具有相同的亮度和对比度特征。同样，不规则的肿瘤形状也是一个严重问题，它会因癌症阶段和肿瘤类型而异。肝脏肿瘤分割主要有两个阶段：识别肝脏，然后分割肿瘤本身。然而，传统的交互式分割方法需要大量的强度级别，而最近提出的基于卷积神经网络（CNN）的交互式分割方法在肝肿瘤图像上的表现较低。本研究提供了一种独特的基于深度学习的分割方法，即基于Coot极限学习模型的方法，该方法在结果上显示出高效率，并且还能从公开可用的肝脏图像数据中检测肿瘤。具体而言，该研究通过少量额外的用户点击来处理初始分割，通过提出的测地距离编码方法合并内边界点来生成改进的分割。最后，使用极限学习模型进行分类，分类器的参数通过Coot优化算法（COA）进行了理想选择。在3D-IRCADb1数据集上，该研究评估了分割质量指标DICE和准确率，发现与肝色和肿瘤分离的方法相比有改进。
