Deformable MRI-Ultrasound Registration via Attribute Matching and Mutual-Saliency Weighting for Image-Guided Neurosurgery

Machado, Inês; Toews, Matthew; Luo, Jie; Unadkat, Prashin; Essayed, Walid Ibn; George, Elizabeth; Teodoro, Pedro; Carvalho, Herculano; Martins, Jorge; Golland, Polina; Pieper, Steve; Frisken, Sarah; Golby, Alexandra J.; Wells, William M.; Ou, Yangming

doi:10.1007/978-3-030-01045-4_20

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…The performance of landmark-based methods in non-linear image registration depends on both finding enough landmarks that cover the entire volume, and correctly finding their corresponding landmarks in the second volume. The voxel-wise attribute-based method of Machado et al [13] (team cDRAMMS) did relatively well despite the fact that iUS and MRI have drastically different salient features, and ranked third in a tie with Drobny et al [12] (team NiftyReg). The top three algorithms in this challenge [12][13][14] were all intensity-based techniques, which calculated a dense transformation map by utilizing intensity values at all locations.…”

Section: Discussionmentioning

confidence: 99%

“…Machado et al [13] extended the Deformable Registration via Attribute Matching and Mutual-Saliency Weighting (DRAMMS) algorithm [19], a general-purpose algorithm [20], specifically for the US-MRI registration problem, which they termed as correlation-similarity DRAMMS or cDRAMMS. They released it at https://www.nitrc.org/projects/dramms/ (version 1.5.1).…”

Section: A Team Cdrammsmentioning

confidence: 99%

“…The distances between homologous anatomical landmarks between iUS and MRI were used to assess and rank the registration quality. The CuRIOUS2018 challenge received 8 initial submissions [10][11][12][13][14][15][16][17]. Seven teams validated their methods on the testing data, and six participated in the final ranking.…”

mentioning

confidence: 99%

“…Seven teams validated their methods on the testing data, and six participated in the final ranking. The submissions cover a wide variety of approaches, including the latest registration metrics [7,13], optimization approaches [12], and deep learning techniques [17]. This paper describes the organization, submitted algorithms, and results for the challenge, and further discusses the current challenges and potential future directions of tissue shift correction in US-guided brain tumor surgery.…”

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confidence: 99%

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Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge

Xiao

Maier

Wein

et al. 2020

IEEE Trans. Med. Imaging

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In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the presurgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.

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Section: Discussionmentioning

confidence: 99%

Section: A Team Cdrammsmentioning

confidence: 99%

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Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge

Xiao

Maier

Wein

et al. 2020

IEEE Trans. Med. Imaging

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“…Previous studies of medical image registration can be categorized into classical and learning-based methods [10][11][12]. Classical MRI to US image registration approaches include various choices of similarity metrics such as Correlation Coefficient (CC), and Correlation Ratio (CR), Mutual Information (MI), Normalized Correlation Coefficient (NCC), Self-Similarity Correlation (SSC), and Linear Correlation of Linear Combination (LC 2 ) [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. One major drawback of the traditional methods is the high computational cost required to align every 3D MRI and iUS pair even with the efficient implementation on modern graphical processing units (GPUs).…”

Section: Introductionmentioning

confidence: 99%

iRegNet: Non-Rigid Registration of MRI to Interventional US for Brain-Shift Compensation Using Convolutional Neural Networks

Zeineldin¹,

Karar²,

Elshaer³

et al. 2021

IEEE Access

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Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multilocation databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance.

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WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network

Almahfouz Nasser,

Kurian,

Meena

et al. 2023

Lecture Notes in Computer Science

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Deformable MRI-Ultrasound Registration via Attribute Matching and Mutual-Saliency Weighting for Image-Guided Neurosurgery

Cited by 6 publications

References 11 publications

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge

iRegNet: Non-Rigid Registration of MRI to Interventional US for Brain-Shift Compensation Using Convolutional Neural Networks

WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network

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