Zaridis Dimitrios I, Mylona Eugenia, Tsiknakis Nikos, Tachos Nikolaos S, Matsopoulos George K, Marias Kostas, Tsiknakis Manolis, Fotiadis Dimitrios I
Biomedical Research Institute, FORTH, 45110 Ioannina, Greece.
Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece.
Patterns (N Y). 2024 May 15;5(7):100992. doi: 10.1016/j.patter.2024.100992. eCollection 2024 Jul 12.
Prostate cancer diagnosis and treatment relies on precise MRI lesion segmentation, a challenge notably for small (<15 mm) and intermediate (15-30 mm) lesions. Our study introduces ProLesA-Net, a multi-channel 3D deep-learning architecture with multi-scale squeeze and excitation and attention gate mechanisms. Tested against six models across two datasets, ProLesA-Net significantly outperformed in key metrics: Dice score increased by 2.2%, and Hausdorff distance and average surface distance improved by 0.5 mm, with recall and precision also undergoing enhancements. Specifically, for lesions under 15 mm, our model showed a notable increase in five key metrics. In summary, ProLesA-Net consistently ranked at the top, demonstrating enhanced performance and stability. This advancement addresses crucial challenges in prostate lesion segmentation, enhancing clinical decision making and expediting treatment processes.
前列腺癌的诊断和治疗依赖于精确的MRI病变分割，这对于小（<15毫米）和中等大小（15 - 30毫米）的病变来说是一项特别具有挑战性的任务。我们的研究引入了ProLesA-Net，这是一种具有多尺度挤压与激励以及注意力门机制的多通道3D深度学习架构。在两个数据集上与六个模型进行对比测试时，ProLesA-Net在关键指标上表现显著优于其他模型：骰子系数提高了2.2%，豪斯多夫距离和平均表面距离改善了0.5毫米，召回率和精确率也有所提高。具体而言，对于15毫米以下的病变，我们的模型在五个关键指标上有显著提升。总之，ProLesA-Net始终名列前茅，展现出更高的性能和稳定性。这一进展解决了前列腺病变分割中的关键挑战，提升了临床决策水平并加快了治疗进程。
