Biology subjects

Farnia, P.

Publications and source records attributed to Farnia, P..

3 recordsLinked to original sources

bioRxiv2022

D2BGAN: Dual Discriminator Bayesian Generative Adversarial Network for Deformable MR-Ultrasound Registration Applied to Brain Shift compensation

PurposeIn neurosurgery, image guidance is provided based on the patient to pre-operative data registration with a neuronavigation system. However, the brain shift phenomena invalidate the accuracy of the navigation system during neurosurgery. One of the most common approaches for brain shift compensation is using intra-operative ultrasound (iUS) imaging followed by registration of iUS with pre-operative magnetic resonance (MR) images. While, due to the unpredictable nature of brain deformation and the low quality of ultrasound images, finding a satisfactory multimodal image registration approach remains a challenging task. MethodsWe proposed a new automatic unsupervised end-to-end MR-iUS registration approach based on the Dual Discriminator Bayesian Generative Adversarial Network (D2BGAN). The proposed network consists of two discriminators and is optimized by introducing a Bayesian loss function to improve the generator functionality and adding a mutual information loss function to the discriminator for similarity measurement. An evaluation was performed using the RESECT training dataset based on the organizers manual landmarks. ResultsThe mean Target Registration Error (mTRE) after MR-iUS registration using D2BGAN reached 0.75{+/-}0.3 mm. The D2BGAN illustrated a clear advantage by 85% improvement in the mTRE of MR-iUS registration over the initial error. Also, the results confirmed that the proposed Bayesian loss function rather than the typical loss function outperforms the accuracy of MR-iUS registration by 23%. ConclusionThe D2BGAN improved the registration accuracy while allowing us to maintain the intensity and anatomical information of the input images in the registration process. It promotes the advancement of deep learning-based multi-modality registration techniques.

bioengineering↗

bioRxiv2021

Photoacoustic-MR Images Registration Based on Co-Sparse Analysis Model to Compensate for Brain Shift

Brain shift is an important obstacle to the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging to update the image-guided surgery systems. However, due to the innate limitations of the current imaging modalities, accurate brain shift compensation continues to be a challenging task. In this study, the application of intra-operative photoacoustic imaging and registration of the intra-operative photoacoustic with pre-operative MR images is proposed to compensate for brain deformation. Finding a satisfactory registration method is challenging due to the unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for photoacoustic -MR image registration, which can capture the interdependency of the two modalities. The proposed algorithm works based on the minimization of the mapping transform via a pair of analysis operators that are learned by the alternating direction method of multipliers. The method was evaluated using experimental phantom and ex-vivo data obtained from the mouse brain. The results of phantom data show about 63% improvement in target registration error in comparison with the commonly used normalized mutual information method. Results proved that intra-operative photoacoustic images could become a promising tool when the brain shift invalidated pre-operative MRI.

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↗