Zossou Vincent-Béni Sèna, Rodrigue Gnangnon Freddy Houéhanou, Biaou Olivier, de Vathaire Florent, Allodji Rodrigue S, Ezin Eugène C
Université Paris-Saclay, UVSQ, Univ. Paris-Sud, CESP, Équipe Radiation Epidemiology, 94805, Villejuif, France.
Centre de recherche en épidémiologie et santé des populations (CESP), U1018, Institut national de la santé et de la recherche médicale (INSERM), 94805, Villejuif, France.
J Imaging Inform Med. 2025 Apr;38(2):873-886. doi: 10.1007/s10278-024-01192-w. Epub 2024 Sep 3.
Liver cancer, a leading cause of cancer mortality, is often diagnosed by analyzing the grayscale variations in liver tissue across different computed tomography (CT) images. However, the intensity similarity can be strong, making it difficult for radiologists to visually identify hepatocellular carcinoma (HCC) and metastases. It is crucial for the management and prevention strategies to accurately differentiate between these two liver cancers. This study proposes an automated system using a convolutional neural network (CNN) to enhance diagnostic accuracy to detect HCC, metastasis, and healthy liver tissue. This system incorporates automatic segmentation and classification. The liver lesions segmentation model is implemented using residual attention U-Net. A 9-layer CNN classifier implements the lesions classification model. Its input is the combination of the results of the segmentation model with original images. The dataset included 300 patients, with 223 used to develop the segmentation model and 77 to test it. These 77 patients also served as inputs for the classification model, consisting of 20 HCC cases, 27 with metastasis, and 30 healthy. The system achieved a mean Dice score of in segmentation and a mean accuracy of in classification, both in the test phase. The proposed method is a preliminary study with great potential in helping radiologists diagnose liver cancers.
肝癌是癌症死亡的主要原因之一，通常通过分析不同计算机断层扫描（CT）图像中肝脏组织的灰度变化来诊断。然而，强度相似性可能很强，这使得放射科医生很难通过视觉识别肝细胞癌（HCC）和转移瘤。准确区分这两种肝癌对于管理和预防策略至关重要。本研究提出了一种使用卷积神经网络（CNN）的自动化系统，以提高检测HCC、转移瘤和健康肝脏组织的诊断准确性。该系统结合了自动分割和分类。肝脏病变分割模型使用残差注意力U-Net实现。一个9层的CNN分类器实现病变分类模型。其输入是分割模型的结果与原始图像的组合。数据集包括300名患者，其中223名用于开发分割模型，77名用于测试。这77名患者也作为分类模型的输入，包括20例HCC病例、27例转移瘤病例和30例健康病例。在测试阶段，该系统在分割方面的平均Dice分数为 ，在分类方面的平均准确率为 。所提出的方法是一项初步研究，在帮助放射科医生诊断肝癌方面具有巨大潜力。
