Browsing by Author "Imanirakiza, Sylvia"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Needle Segmentation For Real-time Guidance of Minimally Invasive Procedures Using Handheld 2D Ultrasound Systems
    (TechRxiv, 2022) Okwija Mugume, Paul; Nabacwa, Joanitta; Imanirakiza, Sylvia; Bagetuuma Kimbowa, Alvin
    Accurate needle placement is crucial during minimally invasive procedures such as biopsies, regional anesthesia, and fluid aspiration. 2D Ultrasound is widely used for needle guidance during such procedures, however, it has a limited fieldof- view and poor needle visibility for steep insertion angles. In this work, we propose a novel machine learning (ML)- based method for real-time needle segmentation in handheld 2D ultrasound systems. The proposed method involves a fast and simple annotation technique allowing for the labeling of large datasets. It then utilizes the U-Net architecture which is modified to allow for easy integration into a handheld ultrasound system. Two datasets were used in this work, one consisting of B-mode ultrasound videos obtained from human tissue and the other consisting of videos and frames from chicken, porcine and bovine tissue. The model is trained on 1262 frames and evaluated on 209 frames. This approach achieves an Intersection Over Union (IoU) of 0.75 and a dice coefficient of 0.851 on frames obtained from human tissue. The model is integrated into the processing pipeline of a portable ultrasound system and achieves an overall processing speed of about 8 frames per second. The proposed approach outperforms state-of-the-art methods for needle segmentation while achieving real-time integration. This work is a step forward towards real-time needle guidance using machine learning-based algorithms in handheld ultrasound systems.
  • Thumbnail Image
    Item
    Time‑aware deep neural networks for needle tip localization in 2D ultrasound
    (International Journal of Computer Assisted Radiology and Surgery, 2021) Mwikirize, Cosmas; Kimbowa, Alvin B.; Imanirakiza, Sylvia; Katumba, Andrew; Nosher, John L.; Hacihaliloglu, Ilker
    Accurate placement of the needle is critical in interventions like biopsies and regional anesthesia, during which incorrect needle insertion can lead to procedure failure and complications. Therefore, ultrasound guidance is widely used to improve needle placement accuracy. However, at steep and deep insertions, the visibility of the needle is lost. Computational methods for automatic needle tip localization could improve the clinical success rate in these scenarios. Methods We propose a novel algorithm for needle tip localization during challenging ultrasound-guided insertions when the shaft may be invisible, and the tip has a low intensity. There are two key steps in our approach. First, we enhance the needle tip features in consecutive ultrasound frames using a detection scheme which recognizes subtle intensity variations caused by needle tip movement. We then employ a hybrid deep neural network comprising a convolutional neural network and long short-term memory recurrent units. The input to the network is a consecutive plurality of fused enhanced frames and the corresponding original B-mode frames, and this spatiotemporal information is used to predict the needle tip location. Results We evaluate our approach on an ex vivo dataset collected with in-plane and out-of-plane insertion of 17G and 22G needles in bovine, porcine, and chicken tissue, acquired using two different ultrasound systems. We train the model with 5000 frames from 42 video sequences. Evaluation on 600 frames from 30 sequences yields a tip localization error of 0.52 ± 0.06 mm and an overall inference time of 0.064 s (15 fps). Comparison against prior art on challenging datasets reveals a 30% improvement in tip localization accuracy. Conclusion The proposed method automatically models temporal dynamics associated with needle tip motion and is more accurate than state-of-the-art methods. Therefore, it has the potential for improving needle tip localization in challenging ultrasound-guided interventions.