| dc.description.abstract | Renal tumor refers to a localized abnormality that develops in the kidney,
which can be classified as either benign or malignant. Early detection and
treatment of renal tumors are crucial to mitigate future risks and reduce associated
costs. To achieve accurate and efficient early detection and treatment,
computerized tomography (CT) imaging plays a significant role compared to
other diagnostic methods.
In this study, a total of 382 patient data were collected, and preprocessing
was performed on the CT images. A two-stage segmentation approach utilizing a
Three-Dimensional Convolutional Neural Network (3D CNN) architecture called
Deepmedic was employed. The first stage aimed to extract the Region of Interest
(ROI) containing the kidney and the tumor. Subsequently, post-processing was
conducted to eliminate small false positive regions. In the second stage, the ROI
obtained from the first stage was further segmented to delineate the renal tumor.
Finally, an intuitive and user-friendly graphical user interface was developed to
facilitate the ease of use for physicians.
The data used in this study consisted of CT images from patients with renal
tumors, with 60% used for training, 20% for validation, and 20% for test set. The
performance evaluation of the segmented kidney and tumor regions yielded an
average Dice coefficient of 93.82%, an average precision of 94.86%, and an
average recall of 93.66%. For the renal tumor segmentation, the average Dice
coefficient was 88.19%, the average precision was 90.36%, and the average recall
was 88.23%.
By employing deep learning, the model was trained to segment the location
of renal tumors and quantify their volume on CT images, showing favorable
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segmentation performance for the kidney and tumor regions. The developed user
interface further simplified the interaction for physicians, enabling them to
achieve high efficiency within a short timeframe. This research contributes to the
automated segmentation and quantification of renal tumors using deep learning
on CT images, offering valuable support for clinical diagnosis and related
applications. | en_US |