  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.
