Özcan Fırat, Uçan Osman Nuri, Karaçam Songül, Tunçman Duygu
Department of Mechatronics Engineering, Faculty of Technology, Kırklareli University, Kayalı Campus, 39100 Kırklareli, Turkey.
Faculty of Applied Sciences, Altınbaş University, Mahmutbey Dilmenler Str., 26, 34217 Istanbul, Turkey.
Bioengineering (Basel). 2023 Feb 6;10(2):215. doi: 10.3390/bioengineering10020215.
The segmentation of the liver is a difficult process due to the changes in shape, border, and density that occur in each section in computed tomography (CT) images. In this study, the Adding Inception Module-Unet (AIM-Unet) model, which is a hybridization of convolutional neural networks-based Unet and Inception models, is proposed for computer-assisted automatic segmentation of the liver and liver tumors from CT scans of the abdomen. Experimental studies were carried out on four different liver CT image datasets, one of which was prepared for this study and three of which were open (CHAOS, LIST, and 3DIRCADb). The results obtained using the proposed method and the segmentation results marked by the specialist were compared with the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), and accuracy (ACC) measurement parameters. In this study, we obtained the best DSC, JSC, and ACC liver segmentation performance metrics on the CHAOS dataset as 97.86%, 96.10%, and 99.75%, respectively, of the AIM-Unet model we propose, which is trained separately on three datasets (LiST, CHAOS, and our dataset) containing liver images. Additionally, 75.6% and 65.5% of the DSC tumor segmentation metrics were calculated on the proposed model LiST and 3DIRCADb datasets, respectively. In addition, the segmentation success results on the datasets with the AIM-Unet model were compared with the previous studies. With these results, it has been seen that the method proposed in this study can be used as an auxiliary tool in the decision-making processes of physicians for liver segmentation and detection of liver tumors. This study is useful for medical images, and the developed model can be easily developed for applications in different organs and other medical fields.
由于计算机断层扫描（CT）图像中肝脏各部分的形状、边界和密度会发生变化，肝脏分割是一个困难的过程。在本研究中，提出了一种将基于卷积神经网络的Unet和Inception模型相结合的添加Inception模块的Unet（AIM-Unet）模型，用于从腹部CT扫描中对肝脏和肝脏肿瘤进行计算机辅助自动分割。对四个不同的肝脏CT图像数据集进行了实验研究，其中一个是为本研究准备的，另外三个是公开的（CHAOS、LIST和3DIRCADb）。使用所提出的方法获得的结果与专家标记的分割结果，通过Dice相似系数（DSC）、Jaccard相似系数（JSC）和准确率（ACC）测量参数进行比较。在本研究中，我们在所提出的AIM-Unet模型上分别在包含肝脏图像的三个数据集（LiST、CHAOS和我们的数据集）上进行训练，在CHAOS数据集上获得了最佳的DSC、JSC和ACC肝脏分割性能指标，分别为97.86%、96.10%和99.75%。此外，在所提出的模型上分别在LiST和3DIRCADb数据集上计算出DSC肿瘤分割指标为75.6%和65.5%。此外，将AIM-Unet模型在数据集上的分割成功结果与先前的研究进行了比较。通过这些结果可以看出，本研究中提出的方法可以作为医生在肝脏分割和肝脏肿瘤检测决策过程中的辅助工具。本研究对医学图像很有用，并且所开发的模型可以很容易地扩展到不同器官和其他医学领域的应用中。
