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The correct segmentation in ultrasound (US) images/videos is essential since it directly influences future diagnostic applications of breast cancer. Therefore, we propose a lightweight U-Net with a 128 x 128 image input and 1941105 trainable parameters. Our architecture works with a multi-GPU strategy. Parallelization of the image/video processing via GPU hardware allows for optimization of the runtime of the procedures, reducing the executing time by employing multithreading processing through OpenMP and CUDA. The designed architectures were implemented in a parallel programming model to be executed on a multi-GPU NVIDIA GeForce RTX 3090 graphics card with 10496 CUDA cores. The proposed parallel implementation is tested on a workstation with a CUDA-enabled GPU and compared with the non-parallel variant.
This study presents an ablation study of the designed segmentation for the video US database (VBUS) with breast cancer lesions (113 malignant and 75 benign lesions), where the images/videos are segmented in real time.
The designed system was first used in the BUSI database since it contains ground truth references (GT), resulting in a segmentation accuracy of 97.43% and a mean Intersection over Union (IoU) of 95.31%. For database VBUS (videos) that contain breast lesions, the segmentation process generates a video where all lesions are marked in mpeg format. The videos from the VBUS database were segmented to evaluate real-time segmentation, and the inference time of the segmentation was computed.
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