Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration

Mohanty, Sasmita; Dakua, Sarada Prasad

doi:10.1109/access.2022.3154771

Cited by 15 publications

(8 citation statements)

References 66 publications

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“…Multimodal registration is crucial in remote sensing, cross-modal learning, and medical imaging. In particular, treatment planning, computer-aided diagnosis, multimodal diagnostics, surgery simulation, radiation, image-guided interventions, assisted/guided surgery, and illness follow-up are some of the clinically significant applications of deformable registration of CT and MRI images [41] . Evaluating treatment success is a vital stage in the curative treatment of liver neoplasms.…”

Section: Resultsmentioning

confidence: 99%

An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction

Sarvari

Sridevi

2023

Biomedical Signal Processing and Control

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Section: Resultsmentioning

confidence: 99%

An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction

Sarvari

Sridevi

2023

Biomedical Signal Processing and Control

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“…A well built SSM can also be utilized as a basis for segmentation [ 50 ] or to detect pathologies [ 51 ]. The task of point-set registration is especially challenging when facing data noise and deformation [ 47 , 52 ]. In this work, only the outer boundary from the organs was captured to simplify the registration process.…”

Section: Discussionmentioning

confidence: 99%

Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images

Selle,

Kircher,

Schwennen

et al. 2024

BMC Med Inform Decis Mak

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Background Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dimensional data after dimension reduction. Clustering and outlier detection may, however, become less robust or contradictory if multiple high-dimensional data sets per patient exist. Such a scenario is given when the focus is on 3-D data of multiple organs per patient, and a high-dimensional feature matrix per organ is extracted. Methods We use principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and multiple co-inertia analysis (MCIA) combined with bagplots to study the distribution of multi-organ 3-D data taken by computed tomography scans. After point-set registration of multiple organs from two public data sets, multiple hundred shape features are extracted per organ. While PCA and t-SNE can only be applied to each organ individually, MCIA can project the data of all organs into the same low-dimensional space. Results MCIA is the only approach, here, with which data of all organs can be projected into the same low-dimensional space. We studied how frequently (i.e., by how many organs) a patient was classified to belong to the inner or outer 50% of the population, or as an outlier. Outliers could only be detected with MCIA and PCA. MCIA and t-SNE were more robust in judging the distributional location of a patient in contrast to PCA. Conclusions MCIA is more appropriate and robust in judging the distributional location of a patient in the case of multiple high-dimensional data sets per patient. It is still recommendable to apply PCA or t-SNE in parallel to MCIA to study the location of individual organs.

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“…The surgical guidance information incorporated using the navigation information from the real-time interface could provide the following functionality [15][16][17]: 1) visual guidance for the alignment of resection instruments to resection planes, 2) real-time feedback on deviation from planned resection planes, 3) warnings when resection instruments are moved towards critical structures, 4) real-time updates on tumor resection margins.…”

Section: ) Lack Of Standardization In Liver Segmenting Processmentioning

confidence: 99%