Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal.
Centre for Informatics and Systems, University of Coimbra (CISUC), Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal.
Sensors (Basel). 2024 Mar 8;24(6):1752. doi: 10.3390/s24061752.
Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments.
肿瘤学已成为医学领域的一个重要研究领域。计算机断层扫描（CT）已广泛应用于识别和描述病理学，特别是在肿瘤学中，能够精确识别受影响的器官和组织。然而，由于存在伪影以及软组织和相邻器官的密度变化，在 CT 扫描中实现准确的肝脏分割仍然是一个挑战。本文比较了人工智能算法和传统医学图像处理技术，以帮助放射科医生在 CT 扫描中进行肝脏分割，并评估它们的准确性和效率。尽管该领域取得了显著进展，但公共数据集的有限可用性仍然是广泛参与研究和复制方法的重大障碍。未来的方向应集中在增加公共数据集的可访问性、建立标准化的评估指标以及推进三维分割技术的发展。此外，在技术进步和医学专业知识之间保持协作关系至关重要，以确保这些创新不仅在技术上达到准确性，而且还与临床需求和现实保持一致。这种协同作用确保了它们在实际医疗保健环境中的适用性和有效性。
