用于前列腺病变检测评估的多通道3D深度学习架构

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI:10.1109/CAI54212.2023.00071

Dimitrios I. Zaridis, E. Mylona, N. Tachos, K. Marias, M. Tsiknakis, D. Fotiadis

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引用次数: 0

摘要

前列腺癌在磁共振图像上的定位对于准确诊断和治疗至关重要。在本研究中，提出了3种深度学习分割模型从2D到3D空间的转换，以分割MR图像中的前列腺病变。3D Use-Net模型是最好的模型，比第二种模型高出10%的Dice Score和1.79mm的Hausdorff距离。

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Multi-Channel 3D Deep Learning Architectures for Evaluation of Prostate Lesion Detection

The localization of prostate cancer on MR images is of paramount importance for accurate diagnosis and treatment. In the present study, transformations of 3 Deep Learning segmentation models from 2D to 3D space are proposed to segment prostate lesions in MR images. The 3D Use-Net model is the best model outperforming the second by 10% Dice Score and 1.79mm Hausdorff Distance.

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2023 IEEE Conference on Artificial Intelligence (CAI)

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