Ileșan Robert R, Beyer Michel, Kunz Christoph, Thieringer Florian M
Department of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, 4031 Basel, Switzerland.
Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland.
Bioengineering (Basel). 2023 May 17;10(5):604. doi: 10.3390/bioengineering10050604.
Medical image segmentation, whether semi-automatically or manually, is labor-intensive, subjective, and needs specialized personnel. The fully automated segmentation process recently gained importance due to its better design and understanding of CNNs. Considering this, we decided to develop our in-house segmentation software and compare it to the systems of established companies, an inexperienced user, and an expert as ground truth. The companies included in the study have a cloud-based option that performs accurately in clinical routine (dice similarity coefficient of 0.912 to 0.949) with an average segmentation time ranging from 3'54″ to 85'54″. Our in-house model achieved an accuracy of 94.24% compared to the best-performing software and had the shortest mean segmentation time of 2'03″. During the study, developing in-house segmentation software gave us a glimpse into the strenuous work that companies face when offering clinically relevant solutions. All the problems encountered were discussed with the companies and solved, so both parties benefited from this experience. In doing so, we demonstrated that fully automated segmentation needs further research and collaboration between academics and the private sector to achieve full acceptance in clinical routines.
医学图像分割，无论是半自动还是手动进行，都需要耗费大量人力、具有主观性，并且需要专业人员。由于对卷积神经网络（CNNs）有了更好的设计和理解，全自动分割过程最近变得越发重要。考虑到这一点，我们决定开发自己的内部分割软件，并将其与知名公司的系统、一名缺乏经验的用户以及一位专家的分割结果（作为基准真值）进行比较。参与研究的公司提供基于云的选项，在临床常规操作中表现准确（骰子相似系数为0.912至0.949），平均分割时间在3分54秒至85分54秒之间。与性能最佳的软件相比，我们的内部模型准确率达到了94.24%，平均分割时间最短，为2分03秒。在研究过程中，开发内部分割软件让我们初步了解了公司在提供临床相关解决方案时所面临的艰巨工作。我们与这些公司讨论并解决了遇到的所有问题，双方都从这次经历中受益。通过这样做，我们证明了全自动分割需要学术界和私营部门进一步开展研究与合作，以便在临床常规操作中获得全面认可。
