KEYWORDS: Image segmentation, Video, Video acceleration, Education and training, Ultrasonography, Medical imaging, Databases, Breast, Video processing, Machine learning
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.
Childhood leukaemia demands meticulous blood cell analysis for diagnosis, focusing on morphological irregularities like asymmetry and abnormal cell counts. Traditional manual diagnosis via microscopic blood smear images suffers from reduced reliability, time intensiveness, and observer variability. Computer-aided diagnostic (CAD) systems address these challenges. Integrating real-time image pre-processing and segmentation ensures swift operation, reducing the CAD system processing time. This enhances its overall effectiveness, enabling timely medical intervention and better patient outcomes. This study aims to simplify the algorithmic complexity of pre-processing steps, including bilateral filtering and Contrast-Limited Adaptive Histogram Equalization (CLAHE), alongside the segmentation stage involving morphological operations and the watershed algorithm. This work proposes a parallel implementation utilizing OpenMP and CUDA, evaluating its performance using accuracy and Intersection over Union (IoU) metrics along with computing time and algorithmic complexity. It highlights the benefits of parallel processing in enhancing efficiency and and accuracy in blood cell analysis.
The Chest X-Ray imaging as a low resource diagnosing tool that can bring sufficiently information from the thorax, helping to a specialist to find patterns with purpose to diagnose the pneumonia disease. Also, due to the simplicity to obtain these images, Chest X-Ray is the top choice against CT, US, CT, or MRI imaging in paediatric patients. In this work, we propose a novel Pseudo-attention module based on handcraft features. Generating the Region of Interest (ROI) image of the thorax, avoiding the rest of the body and eliminating the labels contained in this type of test. After obtaining the ROI image, it is evaluated with several architectures based on Convolutional Neural Networks such as DenseNET, ResNET and MobileNET. Finally, the designed system employs Grad-Cam algorithm to provide the perceptual image of the relevant features significant in the classification of Pneumonia against Normal class. The system has demonstrated similar or better performance in comparison with the state-of-the-art methods using evaluation metrics such as Accuracy, Precision, Sensibility, and F1 score.
This study proposes a Hybrid CAD system, where the first stage consists of the handcraft segmentation, following a CNN based on the ResNet-34 architecture. In the segmentation stage, the rib cage (thorax region) is extracted using the K-means algorithm. The extraction of the nodules is performed in two steps, those attached to the pleura are found via a hysteresis threshold on the rib cage. The circumscribed and vascular nodules are extracted using morphological operations. The resulting segmentation masks are applied to the test images, decreasing the number of false positives. Finally, the resulting image is splitted of in patches to be classified by the ResNet-34 trained from scratch. Designed CAD system has been implemented on Google Collab platform and a standalone computer with Nvidia RTX 3090. The experiments with different CAD systems were performed on SPIE and LIDC-IDRI datasets demonstrating better performance of designed technique with reduction of false-positive objects.
Since the first quarter of this year, the spread of SARS-CoV-19 virus has been a worldwide health priority. Medical testing consists of Lab studies, PCR tests, CT, PET, which are time-consuming, some countries lack these resources. One medical tool for diagnosis is X-Ray imaging, which is one of the fastest and low-cost resources for physicians to detect and to distinguish among these different diseases. We propose an X-Ray CAD system based on DCNN, using well-known architectures such as DenseNet-201, ResNet-50 and EfficientNet. These architectures are pre-trained on data from Imagenet classification challenge, moreover, using Transfer Learning methods to Fine-Tune the classification stage. The system is capable to visualize the learned recognition patterns applying the GRAD-CAM algorithm aiming to help physicians in seeking hidden features from perceptual vision. The proposed CAD can differentiate between COVID-19, Pneumonia, Nodules and Normal lung X-Ray images.
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