Biology subjects

Jalili Aziz, M.

Publications and source records attributed to Jalili Aziz, M..

2 recordsLinked to original sources

bioRxiv2022

Spatiotemporal analysis of speckle dynamics to track invisible needle in ultrasound sequences using Convolutional Neural Networks

ObjectiveAccurate needle placement to the target point is critical for ultrasound interventions like biopsies and epidural injections. However, aligning the needle to the thin plane of the transducer is a challenging issue as it leads to the decay of visibility by the naked eye. Therefore, we have developed a CNN-based framework to track the needle using the spatiotemporal features of speckle dynamics. MethodsThere are three key techniques to optimize the network for our application. First, we proposed a motion field estimation network (RMF) to extract spatiotemporal features from the stack of consecutive frames. We also designed an efficient network based on the state-of-the-art Yolo framework (nYolo). Lastly, the Assisted Excitation (AE) module was added at the neck of the network to handle imbalance problem. ResultsTen freehand ultrasound sequences are collected by inserting an injection needle steeply into the Ultrasound Compatible Lumbar Epidural Simulator and Femoral Vascular Access Ezono test phantoms. We divided the dataset into two sub-categories. In the second category, in which the situation is more challenging and the needle is totally invisible statically, the angle and tip localization error were 2.43{+/-}1.14{degrees} and 2.3{+/-}1.76 mm using Yolov3+RMF+AE and 2.08{+/-}1.18{degrees} and 2.12{+/-}1.43 mm using nYolo+RMF+AE. Conclusion and significanceThe proposed method has the potential to track the needle in a more reliable operation compared to other state-of-the-art methods and can accurately localize it in 2D B-mode US images in real-time, allowing it to be used in in current ultrasound intervention procedures.

bioengineering↗

bioRxiv2021

Accurate Automatic Glioma Segmentation in Brain MRI images Based on CapsNet

Glioma is a highly invasive type of brain tumor with an irregular morphology and blurred infiltrative borders that may affect different parts of the brain. Therefore, it is a challenging task to identify the exact boundaries of the tumor in an MR image. In recent years, deep learning-based Convolutional Neural Networks (CNNs) have gained popularity in the field of image processing and have been utilized for accurate image segmentation in medical applications. However, due to the inherent constraints of CNNs, tens of thousands of images are required for training, and collecting and annotating such a large number of images poses a serious challenge for their practical implementation. Here, for the first time, we have optimized a network based on the capsule neural network called SegCaps, to achieve accurate glioma segmentation on MR images. We have compared our results with a similar experiment conducted using the commonly utilized U-Net. Both experiments were performed on the BraTS2020 challenging dataset. For U-Net, network training was performed on the entire dataset, whereas a subset containing only 20% of the whole dataset was used for the SegCaps. To evaluate the results of our proposed method, the Dice Similarity Coefficient (DSC) was used. SegCaps and U-Net reached DSC of 87.96% and 85.56% on glioma tumor core segmentation, respectively. The SegCaps uses convolutional layers as the basic components and has the intrinsic capability to generalize novel viewpoints. The network learns the spatial relationship between features using dynamic routing of capsules. These capabilities of the capsule neural network have led to a 3% improvement in results of glioma segmentation with fewer data while it contains 95.4% fewer parameters than U-Net.

bioengineering↗