Deep learning (DL) models are state-of-the-art in segmenting anatomical and disease regions of interest (ROIs) in medical images. Particularly, a large number of DL-based techniques have been reported using chest X-rays (CXRs). However, these models are reportedly trained on reduced image resolutions for reasons related to the lack of computational resources. Literature is sparse in discussing the optimal image resolution to train these models for segmenting the Tuberculosis (TB)-consistent lesions in CXRs. In this study, we investigated the performance variations using an Inception-V3 UNet model using various image resolutions with/without lung ROI cropping and aspect ratio adjustments, and (ii) identified the optimal image resolution through extensive empirical evaluations to improve TB-consistent lesion segmentation performance. We used the Shenzhen CXR dataset for the study which includes 326 normal patients and 336 TB patients. We proposed a combinatorial approach consisting of storing model snapshots, optimizing segmentation threshold and test-time augmentation (TTA), and averaging the snapshot predictions, to further improve performance with the optimal resolution. Our experimental results demonstrate that higher image resolutions are not always necessary, however, identifying the optimal image resolution is critical to achieving superior performance.

翻译：深度学习（DL）模型在医学图像中解剖和疾病感兴趣区域（ROI）分割方面处于前沿水平。特别是在胸部X光片（CXR）的应用中，已有大量基于深度学习的技术被报道。然而，这些模型通常因计算资源不足而在降低的图像分辨率下进行训练。关于在CXR中分割结核病（TB）一致性病灶时训练这些模型的最佳图像分辨率，现有文献讨论较少。本研究采用Inception-V3 UNet模型，探讨了在不同图像分辨率下（包括是否进行肺部ROI裁剪及宽高比调整）的性能变化，并通过广泛的经验评估确定了最佳图像分辨率，以提升TB一致性病灶的分割性能。我们使用了包含326例正常患者和336例结核病患者的深圳CXR数据集。我们提出了一种组合方法，包括存储模型快照、优化分割阈值和测试时增强（TTA），并对快照预测结果进行平均，以进一步在最佳分辨率下提升性能。实验结果表明，更高图像分辨率并非总是必要，但确定最佳图像分辨率对实现卓越性能至关重要。