生物医学图像处理是人工智能和计算机视觉的一个交叉研究领域，提供了复杂和自动化的技术，以解决问题并提升性能。这对于医生利用先进的技术和方法进行疾病的自动诊断并取得准确的诊断结果具有重要意义。医学图像分割用于在早期诊断各种疾病，同时不同种类医学图像的分割是医学图像分析的主要部分，例如分割肝器官或其他人体器官。如今，随着肝癌患者的增加，肝器官分割已经逐渐成为是一个亟待解决的问题。虽然目前许多肝脏的自动分割技术有所发展，但由于肝脏与其他器官位置上模糊背景上复杂，肝脏边界的复杂和肝脏外观的多样性，使得肝脏的分割仍然是一项艰巨的任务。因此，开发一种从 CT 扫描中自动分割肝脏和肿瘤的方法对肝癌诊断分析具有重要意义。因此，在本课题的研究中，我们专注于开发基于 CNN 的训练模型，以基于 ResUNet 分割肝脏和肝脏肿。

首先，我们描述了肝癌疾病和医学模式，以突出肿瘤对人体的影响， 然后描 述了与这些模式相关的各种问题。肝癌是全球最主要的致死癌症之一。从 3D生物医学图像或CT 扫描中自动分割肝脏对于许多临床应用是一项非常重要并且极具挑战性的任务，如手术规划、肝脏疾病、术后分析等，它可以帮助医生更快、更准确地分割肝脏肿瘤。此外，由于肝肿瘤与其他器官在强度值上的重叠和位置上的变异性，进一步增加了从肝脏分割肿瘤的难度。

其次，介绍了一些用于肝脏分割和肿瘤分割的 CNN模型，如 U-Net, ResNet-50 和 VGGNet-19。但我们发现，这些 CNN 训练模型结构复杂，分割边界模糊，仍然不能提供足够的信息来分割肝脏和肿瘤。

使用 CNN 解决了肝脏和肿瘤分割遇到的问题，在IRCAD的3D-IRCADb01数据集上使用ResUNet模型，该数据集包含患者的CT切片以及肝脏、肿瘤和其他身体器官的遮罩。 ResUNet 是 U-Net 和 ResNet  的混合组合，其中使用残差块而不是传统的卷积块。

    在我们的技术中，采用了不同的策略，如3-slice 法；使用VGGNet-19和RestNet-50作为基础网络和ResUNet方法。我们通过检查肝脏和肿瘤的最终结果，对比不同策略组合的效果。数据集包含 10 名女性和 10 名男性，其中 75%的病例中患有肝肿瘤。我们使用两种级联 CNN 方法，一种用于分割肝脏并提取 ROI，第二种用于从第一个 CNN 中提取 ROI 并分割肿瘤。 获得了高达 95%的骰子系数和 99%的真值准确度，结果表明，我们的方法给予最好的 DICE 分数。

最后，重申了研究特点和创新，明确提出的方法提供了更好的效果，更有效的损失函数的基础，交叉数据集的交叉应用，这可以改善从任何数据集分割腹部器官的模式。如利用ResUNet的优点，从DICOM图像中分割心脏和血管，从MRI中分割肝脏等。

Biomedical image processing is a wide area in field of artificial intelligence & computer vision that provide complex and automated technology to get sophisticated performance and solution for problems statements. This is great significance for doctors to diagnosis diseases automatically using the advanced technologies and methods with accurate results. Segmentation of different medical images is the important part of medical images analysis, because medical image segmentation is used to diagnosis different diseases at its earlier stage for example segmentation of liver, and other human organs. Liver segmentation is an important problem because of increasing of liver cancer patient. Although there are many automated techniques have been developed for liver and tumor segmentation, however, segmentation of liver is still a difficult task due to the fuzzy & complex background of the liver position with other organs, complicated boundary and various appearance of liver. Therefore, developing a significance automatic liver and tumor segmentation from CT scans is very important for the analysis of liver cancer diagnoses. That’s why in this research topic we focused to developed trained a CNNs based model to segment the liver and liver’s tumor based on ResUNet.

Firstly, we described about liver cancer disease, medical modalities to highlight the presence of tumor present in human body, and we described about different problems related to these modalities. Liver cancer is one of the most leading cause of cancer death in worldwide. Automatic liver segmentation from 3D biomedical images or CT scan is an essential challenging task for many clinical applications, such as surgical planning, hepatic diseases, postoperative analysis, and it can help doctors, radiologists to segment the liver tumor faster, and accurate analyses. Moreover, segmenting tumor from liver increases further dimensionality of difficulty because of overlap in intensity values and variability in position with other organs.

Secondly, we highlight some of trained CNNs models, which are using for the liver segmentation and tumor segmentation like U-Net, ResNet-50 and VGGNet-19. But we checked that these CNNs trained models are still not providing enough information to segment liver and tumor because of its fuzzy boundaries and complicated structure. 

Thirdly, we used CNNs to overcome all the obstacles for segmentation of liver and tumor, we used ResUNet model on the 3D-IRCADb01 dataset by IRCAD which contains CT slices for patients along with masks for liver, tumors and other body organs. ResUNet is hybrid combination between the U-Net & ResNet, where it is uses Residual blocks rather than the traditional convolution blocks.

In our techniques, we apply different strategies like 3-slice input; we used VGGNet-19, and RestNet-50 as base network and ResUNet method. We compare the combination of different strategies to check the final results of the liver and tumors separately. Our dataset contains 10 women and 10 men having hepatic tumors in 75% of cases ResUNet model consists of stacked layers of modified residual building blocks. We used the 2 cascaded CNNs approach one for segmenting the liver and extracting the ROI & the second one we use for extracted ROI from the first CNN and segment the tumors. We achieved dice coefficient of up to 95% and True value Accuracy of up to 99% and our comparison results show our method gives the best DICE score. 

Lastly, we high light research characteristics and innovations that explicit the proposed approaches provide better improvement, basis towards more effective loss functions, and Cross application for cross-datasets, which can be improved for segmentation of abdominal organs from any dataset’s modalities. Such as segmentation of heart and blood vessels from DICOM images, segmentation of liver from MRI, etc. by taking the advantages from ResUNet.

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