CT-MRI Dual Information Registration for the Diagnosis of Liver Cancer: A Pilot Study Using Point-based Registration

Rahimi, A; Khalil, Azira; Amir, Faisal; Lai, Khin Wee

doi:10.2174/1573405617666210825155659

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…In future work, we want to extend the framework to US 3D images and perform with Esaote quality department and expert radiologists a clinical validation of the method through more formalised qualitative survey and evaluation methods [ 16 , 34 ] through an interdisciplinary approach that involves engineering, medical science, physics, and computer science.…”

Section: Discussionmentioning

confidence: 99%

Super-resolution of 2D ultrasound images and videos

Cammarasana

Nicolardi

Patanè

2023

Med Biol Eng Comput

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This paper proposes a novel deep-learning framework for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction. To this end, we up-sample the acquired low-resolution image through a vision-based interpolation method; then, we train a learning-based model to improve the quality of the up-sampling. We qualitatively and quantitatively test our model on different anatomical districts (e.g., cardiac, obstetric) images and with different up-sampling resolutions (i.e., 2X, 4X). Our method improves the PSNR median value with respect to SOTA methods of $$1.7\%$$ 1.7 % on obstetric 2X raw images, $$6.1\%$$ 6.1 % on cardiac 2X raw images, and $$4.4\%$$ 4.4 % on abdominal raw 4X images; it also improves the number of pixels with a low prediction error of $$9.0\%$$ 9.0 % on obstetric 4X raw images, $$5.2\%$$ 5.2 % on cardiac 4X raw images, and $$6.2\%$$ 6.2 % on abdominal 4X raw images. The proposed method is then applied to the spatial super-resolution of 2D videos, by optimising the sampling of lines acquired by the probe in terms of the acquisition frequency. Our method specialises trained networks to predict the high-resolution target through the design of the network architecture and the loss function, taking into account the anatomical district and the up-sampling factor and exploiting a large ultrasound data set. The use of deep learning on large data sets overcomes the limitations of vision-based algorithms that are general and do not encode the characteristics of the data. Furthermore, the data set can be enriched with images selected by medical experts to further specialise the individual networks. Through learning and high-performance computing, the proposed super-resolution is specialised to different anatomical districts by training multiple networks. Furthermore, the computational demand is shifted to centralised hardware resources with a real-time execution of the network’s prediction on local devices.

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Section: Discussionmentioning

confidence: 99%

Super-resolution of 2D ultrasound images and videos

Cammarasana

Nicolardi

Patanè

2023

Med Biol Eng Comput

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“…Since the tumor cannot be directly registered, the registration is done on the liver, thus requiring radiologists to manually delineate the liver on the CT in order to evaluate and potentially refine the quality of the registration. As registration has proven to be a tedious and time-consuming task for clinicians, 5,6 various semi-automatic and fully automatic liver registration methods have been studied and can be found in the literature. Conventional registration methods can be categorized 7 based on image dimensionality (2D, 3D, or 4D), modalities involved (mono-or multimodal), transformation models (rigid or non-rigid), and the fundamental approach used, whether feature-based or intensity-based.…”

Section: Introductionmentioning

confidence: 99%

CT-MRI liver registration for selective internal radiation therapy

Fragkiadakis,

Quinton,

Popoff

et al. 2024

Medical Imaging 2024: Image Processing

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Selective internal radiation therapy (SIRT) is a targeted treatment for liver tumors, particularly hepatocellular carcinoma (HCC), that involves the precise delivery of radioactive microspheres to the tumor's blood supply. Image registration is critical to SIRT, ensuring precise alignment of diagnostic images with the treatment plan to optimize microsphere delivery and minimize side effects.In the context of SIRT, our goal was to develop a fully-automatic hybrid registration pipeline, using liver segmentation masks from a 3D UNet model to achieve performance comparable to expert registrations. The pipeline combines conventional and deep learning methods for automatic alignment of pre-treatment magnetic resonance images (MRI) on single-photon emission computed tomography (SPECT)/computed tomography (CT) images. This hybrid pipeline, which uses conventional global rigid registration and a deep learning-based approach for local deformation, outperformed conventional and manual expert registration. Quantitative assessment on a dataset of 69 HCC patients showed an improved Dice similarity coefficient (DSC) of 0.928 compared to 0.917 with the conventional methods. A subset analysis of 61 patients with expert registrations showed a mean DSC of 0.922, while our proposed method remained at a mean DSC of 0.928.These results demonstrate the effectiveness of our hybrid approach in achieving accurate liver registration, which is critical for precise microsphere delivery during SIRT. The improvement over conventional methods highlights the potential of incorporating deep learning techniques into multimodal liver registration, thereby improving the overall quality and effectiveness of SIRT in the management of HCC. Our method provides clinicians with a reliable and automated registration pipeline that can positively optimize treatment planning and reduce the burden of manual registration. As a result, our hybrid approach holds promise for more accurate and precise registration results.

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“…Medical image segmentation plays a vital role in computer‐aided diagnosis and intelligent medical treatment. It can preprocess medical images to achieve image registration [1–3], improve the clarity of anatomical or pathological structures in images and the efficiency and accuracy of doctor's diagnosis. However, unlike natural images, there are large differences among medical images due to the influence of image acquisition equipment and the differences in the organs themselves.…”

Section: Introductionmentioning

confidence: 99%

“…For image-level labels, the task mainly focuses on how to use image-level classification labels to generate pixel-level pseudo-labels that can train segmentation networks. Usually, class activation map (CAM) is used as FIGURE 1 Two processes of CAM generation pseudo-labels. The generation of CAM can be mainly divided into two processes, as shown in Figure 1.…”

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confidence: 99%

Global weighted average pooling network with multilevel feature fusion for weakly supervised brain tumor segmentation

Xuan

et al. 2022

IET Image Processing

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Medical image segmentation plays a vital role in computer‐aided diagnosis and intelligent medical treatment. It can preprocess medical images to help doctors better diagnose diseases. Class activation map (CAM) is an important technology in weakly supervised segmentation, which can achieve image segmentation without pixel‐level label training. This technology can well meet the needs of medical image segmentation. However, CAM obtaining is still unperfect due to global average pooling (GAP). GAP will cause important and non‐important regions to be given equal attention during the training process. So, CAM cannot demarcate the boundary of the target regions well. In order to solve this problem, a global weighted average pooling network fusing the grayscale information of medical images is proposed. The proposed network can solve the problem that GAP has the same concern for important regions and non‐important regions of the feature map, because the different weights can be learned for different positions of the feature map before the GAP in the proposed model. At the same time, because of the grayscale difference between the tumor area and the non‐tumor area in the brain tumor image, the low‐level grayscale information of the medical image is fused with the high‐level semantic information extracted by the network to learn the weights. This operation gives full play to the advantages of feature maps of different levels. The experiment results on the popular medical image dataset BraTS2019 show that the proposed method can well improve the performance of CAM and help CAM fit the boundaries of objects. Meanwhile, in the DSC evaluation, the proposed method achieves a score of 64.1%, which is a 4.6% improvement over a recent research method.

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CT-MRI Dual Information Registration for the Diagnosis of Liver Cancer: A Pilot Study Using Point-based Registration

Cited by 7 publications

References 15 publications

Super-resolution of 2D ultrasound images and videos

Super-resolution of 2D ultrasound images and videos

CT-MRI liver registration for selective internal radiation therapy

Global weighted average pooling network with multilevel feature fusion for weakly supervised brain tumor segmentation

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