An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measure

Begum, Nasra; Badshah, Noor; Rada, Lavdie; Ademaj, Adela; Ashfaq, Muniba; Atta, Hadia

doi:10.1016/j.aej.2022.06.018

Cited by 7 publications

(2 citation statements)

References 28 publications

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shows the maximum distance from the calculated minimum distances from the boundary X to the boundary Y , while

represents the maximum distance from the calculated minimum distances from boundary Y to boundary X . The Hausdorff distance (HD) is extremely sensitive to outliers [ 30 ]. As a result, in the field of medical science, HD95 is used.…”

Section: Methodsmentioning

confidence: 99%

DRU-Net: Pulmonary Artery Segmentation via Dense Residual U-Network with Hybrid Loss Function

Zulfiqar

Stanuch

Wodziński

et al. 2023

Sensors

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The structure and topology of the pulmonary arteries is crucial to understand, plan, and conduct medical treatment in the thorax area. Due to the complex anatomy of the pulmonary vessels, it is not easy to distinguish between the arteries and veins. The pulmonary arteries have a complex structure with an irregular shape and adjacent tissues, which makes automatic segmentation a challenging task. A deep neural network is required to segment the topological structure of the pulmonary artery. Therefore, in this study, a Dense Residual U-Net with a hybrid loss function is proposed. The network is trained on augmented Computed Tomography volumes to improve the performance of the network and prevent overfitting. Moreover, the hybrid loss function is implemented to improve the performance of the network. The results show an improvement in the Dice and HD95 scores over state-of-the-art techniques. The average scores achieved for the Dice and HD95 scores are 0.8775 and 4.2624 mm, respectively. The proposed method will support physicians in the challenging task of preoperative planning of thoracic surgery, where the correct assessment of the arteries is crucial.

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“…

shows the maximum distance from the calculated minimum distances from the boundary X to the boundary Y , while

Section: Methodsmentioning

confidence: 99%

DRU-Net: Pulmonary Artery Segmentation via Dense Residual U-Network with Hybrid Loss Function

Zulfiqar

Stanuch

Wodziński

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Medical image registration refers to seeking a (or a series of) spatial transformation (or transformations) for a medical image to make it spatially corresponding to another medical image in a point-by-point manner. Medical image registration can be applied in various areas such as image guidance (Silva et al 2016), motion tracking (Mori et al 2002, Marami et al 2016, Eckera et al 2022, segmentation (Chen and Lu 2019, Lin et al 2019, Begum et al 2022, dose accumulation (Chetty and Rosu-Bubulac 2019, Smolders et al 2022) and image reconstruction (Dang et al 2014, Dong et al 2021.…”

Section: Introductionmentioning

confidence: 99%

Topologically preserved registration of 3D CT images with deep networks

et al. 2023

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Objective. Computed Tomography (CT) image registration makes fast and accurate imaging-based disease diagnosis possible. We aim to develop a framework which can perform accurate local registration of organs in 3D CT images while preserving the topology of transformation. Approach. In this framework, the Faster R-CNN method is first used to detect local areas containing organs from fixed and moving images whose results are then registered with a weakly supervised deep neural network. In this network, a novel 3D channel coordinate attention (CA) module is introduced to reduce the loss of position information. The image edge loss and the organ labelling loss are used to weakly supervise the training process of our deep network, which enables the network learning to focus on registering organs and image structures. An intuitive inverse module is also used to reduce the folding of deformation field. More specifically, the folding is suppressed directly by simultaneously maximizing forward and backward registration accuracy in the image domain rather than indirectly by measuring the consistency of forward and inverse deformation fields as usual. Main results. Our method achieves an average dice similarity coefficient (DSC) of 0.954 and an average Similarity (Sim) of 0.914 on publicly available liver datasets (LiTS for training and Sliver07 for testing) and achieves an average DSC of 0.914 and an average Sim of 0.947 on our home-built left ventricular myocardium (LVM) dataset. Significance. Experimental results show that our proposed method can significantly improve the registration accuracy of organs such as the liver and LVM. Moreover, our inverse module can intuitively improve the inherent topological preservation of transformations.

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