G Suganeshwari, Appadurai Jothi Prabha, Kavin Balasubramanian Prabhu, C Kavitha, Lai Wen-Cheng
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India.
Computer Science and Engineering Department, Kakatiya Institute of Technology and Science, Warangal 506015, Telangana, India.
Biomedicines. 2023 Apr 28;11(5):1309. doi: 10.3390/biomedicines11051309.
Liver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides greater insight into human structures than traditional X-rays, which are typically used to make the diagnosis. Often, the final product of a CT scan is a three-dimensional image constructed from a series of interlaced two-dimensional slices. Remember that not all slices deliver useful information for tumor detection. Recently, CT scan images of the liver and its tumors have been segmented using deep learning techniques. The primary goal of this study is to develop a deep learning-based system for automatically segmenting the liver and its tumors from CT scan pictures, and also reduce the amount of time and labor required by speeding up the process of diagnosing liver cancer. At its core, an Encoder-Decoder Network (En-DeNet) uses a deep neural network built on UNet to serve as an encoder, and a pre-trained EfficientNet to serve as a decoder. In order to improve liver segmentation, we developed specialized preprocessing techniques, such as the production of multichannel pictures, de-noising, contrast enhancement, ensemble, and the union of model predictions. Then, we proposed the Gradational modular network (GraMNet), which is a unique and estimated efficient deep learning technique. In GraMNet, smaller networks called SubNets are used to construct larger and more robust networks using a variety of alternative configurations. Only one new SubNet modules is updated for learning at each level. This helps in the optimization of the network and minimizes the amount of computational resources needed for training. The segmentation and classification performance of this study is compared to the Liver Tumor Segmentation Benchmark (LiTS) and 3D Image Rebuilding for Comparison of Algorithms Database (3DIRCADb01). By breaking down the components of deep learning, a state-of-the-art level of performance can be attained in the scenarios used in the evaluation. In comparison to more conventional deep learning architectures, the GraMNets generated here have a low computational difficulty. When associated with the benchmark study methods, the straight forward GraMNet is trained faster, consumes less memory, and processes images more rapidly.
肝癌是全球所有癌症中第六大最常见的癌症。计算机断层扫描（CT）是一种非侵入性分析成像传感系统，与传统的X射线相比，它能更深入地洞察人体结构，传统X射线通常用于进行诊断。通常，CT扫描的最终产物是由一系列交错的二维切片构建而成的三维图像。要记住，并非所有切片都能为肿瘤检测提供有用信息。最近，已经使用深度学习技术对肝脏及其肿瘤的CT扫描图像进行了分割。本研究的主要目标是开发一种基于深度学习的系统，用于从CT扫描图片中自动分割肝脏及其肿瘤，并通过加快肝癌诊断过程来减少所需的时间和人力。其核心是，一个编码器 - 解码器网络（En - DeNet）使用基于UNet构建的深度神经网络作为编码器，并使用预训练的EfficientNet作为解码器。为了改进肝脏分割，我们开发了专门的预处理技术，如多通道图片生成、去噪、对比度增强、集成以及模型预测的合并。然后，我们提出了梯度模块化网络（GraMNet），这是一种独特且估计有效的深度学习技术。在GraMNet中，称为子网络（SubNets）的较小网络用于使用各种替代配置构建更大、更强大的网络。在每个级别仅更新一个新的子网络模块用于学习。这有助于网络的优化，并最小化训练所需的计算资源量。本研究的分割和分类性能与肝脏肿瘤分割基准（LiTS）和算法比较的三维图像重建数据库（3DIRCADb01）进行了比较。通过分解深度学习的组件，可以在评估中使用的场景中达到最先进的性能水平。与更传统的深度学习架构相比，这里生成的GraMNets具有较低的计算难度。与基准研究方法相关联时，简单的GraMNet训练速度更快，消耗内存更少，并且处理图像更快。
