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Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?

Sivaramakrishnan Rajaraman, Feng Yang, Ghada Zamzmi

    Arxiv
    |February 15, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Optimal image resolution is critical for segmenting tuberculosis lesions in chest X-rays using deep learning models. Higher resolutions are not always necessary, but finding the best resolution significantly improves diagnostic performance.

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    Area of Science:

    • Medical imaging analysis
    • Artificial intelligence in healthcare
    • Radiology and diagnostics

    Background:

    • Deep learning models excel at medical image segmentation but are often trained on reduced resolutions due to computational constraints.
    • Optimal image resolution for segmenting tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) remains under-explored.
    • This study addresses the gap by investigating the impact of image resolution on TB lesion segmentation performance.

    Approach:

    • Evaluated an Inception-V3 UNet model across various image resolutions, with and without lung region of interest (ROI) cropping and aspect ratio adjustments.
    • Utilized the Shenzhen CXR dataset, comprising 326 normal and 336 TB patient images.
    • Developed a combinatorial approach involving model snapshot averaging, optimized segmentation thresholds, and test-time augmentation (TTA) to enhance performance.

    Key Points:

    • Empirical evaluation identified a critical optimal image resolution for superior TB lesion segmentation.
    • Performance variations were analyzed concerning different image resolutions and preprocessing techniques.
    • The proposed combinatorial approach further boosted segmentation accuracy at the optimal resolution.

    Conclusions:

    • Higher image resolutions are not universally required for effective deep learning-based TB lesion segmentation in CXRs.
    • Identifying and utilizing the optimal image resolution is crucial for achieving superior segmentation performance.
    • This research provides valuable insights for optimizing deep learning models in medical image analysis for tuberculosis detection.