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Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images.

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    We developed novel deep learning networks for accurate lung tumor segmentation and volume tracking from CT scans. Our incremental-MRRN method shows high accuracy, aiding in automated tumor monitoring during cancer therapy.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Accurate volumetric lung tumor segmentation and tracking are crucial for therapy response monitoring.
    • Current methods may lack precision in longitudinal tumor volume assessment.

    Purpose of the Study:

    • To develop and evaluate novel multiple resolution residually connected network (MRRN) formulations for lung tumor segmentation.
    • To enable accurate automated tracking of tumor volume changes from computed tomography (CT) images.

    Main Methods:

    • Developed two MRRN formulations: incremental-MRRN and dense-MRRN, combining multi-resolution and multi-level features.
    • Evaluated on 1210 non-small cell lung tumors/nodules from TCIA, MSKCC, and LIDC datasets.
    • Segmentation accuracy assessed using Dice Similarity Coefficient (DSC), Hausdorff distances, sensitivity, and precision.

    Main Results:

    • The incremental-MRRN achieved high DSC values: 0.74±0.13 (TCIA), 0.75±0.12 (MSKCC), and 0.68±0.23 (LIDC).
    • No significant difference was found in volumetric tumor change estimations compared to expert segmentation.
    • The method enables accurate, automated identification and serial measurement of lung tumor volumes.

    Conclusions:

    • A multi-scale CNN approach (incremental-MRRN) provides accurate volumetric lung tumor segmentation.
    • This automated method facilitates precise monitoring of tumor volume changes in clinical practice.
    • The developed networks are effective for tracking tumor response to therapy using CT imaging.