Convex hull matching and hierarchical decomposition for multimodality medical image registration

Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations

BACKGROUND

The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking.

OBJECTIVE

This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress.

METHODS

Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances accuracy and robustness.

RESULTS

Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT.

FUTURE DIRECTIONS

The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain.

CONCLUSION

Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice.

Application of block matching method-based Echocardiography combined with serum NT-PROBNP level detection in the early prediction of PDA in premature infants

Objectives:

The paper uses block matching method combined with echocardiography to explore the value of N-terminal pro-brain natriuretic peptide (NT-proBNP) in predicting symptomatic patent ductus arteriosus (PDA) in preterm infants.

Methods:

We selected premature infants born between February 2019 and March 2020, gestational age ≤32 weeks, and echocardiography within 48 hours to determine the presence of arterial ducts as the research object, monitor their clinical manifestations, and detect serum at three and five days after birth The level of NT-proBNP was checked with echocardiography, and the children were divided into PDA group and asymptomatic patent ductus arteriosus (aPDA) group according to the clinical manifestations and echocardiographic.

Results:

The area under the ROC curve of PDA predicted by serum NT-proBNP level at 3 days after birth was 0.949, the cut-off value was 27035pg/mL, the sensitivity was 92.3%, and the specificity was 94.6%; serum NT-proBNP level at 5 days after birth predicted the ROC curve of PDA The lower area is 0.924, the critical value is 6411 pg/mL, the sensitivity is 92.3%, and the specificity is 92.9%.

Conclusion:

NT-proBNP may be a quantitative indicator of arterial duct shunt; the detection of serum NT-proBNP levels at 3 and 5 days after birth is helpful for early prediction of PDA.

Local incompressible registration for liver ablation surgery assessment

PURPOSE

In liver microwave ablation (MWA) surgery, the ablation area covers the tumor to generate tissue necrosis and treat the cancer. As the liver deforms during the operation, deviation between the target area determined during preoperative planning and the resultant ablation area is inevitable. Therefore, an accurate assessment of tumor coverage is crucial for treatment. Through registration between the pre- and postoperative livers, the ablation area is warped on the preoperative liver for the computation of tumor coverage. However, large deformations between the pre- and postoperative livers are caused by multiple factors, and these diverse deformations make registration a challenging task. The purpose of this paper was to develop an automatic method that can accurately register post- to preoperative livers.

METHODS

In the proposed method, nonrigid deformations caused by respiratory movement and edema are separately considered and estimated by the local incompressible model in the registration of livers. The pre- and postoperative livers are first aligned by a rigid registration based on a convex hull. In the nonrigid registrations, local incompressible constraints are then set on the liver and the ablation area to estimate the deformations caused by respiratory movement and edema, respectively. The concatenation of the rigid and nonrigid deformations is used to warp the ablation area on the preoperative liver.

RESULTS

The proposed method was evaluated using clinical CT datasets from 20 patients. The Dice similarity coefficient (DSC) between the preoperative and warped postoperative livers is 94.35%, the mean surface distance (MSD) between the livers is 1.65 mm, the mean Hausdorff distance (HDD) between the livers is 3.36 mm, and the mean corresponding distance (MCD) between the corresponding landmarks is 1.70 mm. Compared with five other state-of-the-art methods, the proposed method achieves automatic ablation assessment with highly accurate registration.

CONCLUSIONS

The proposed method achieves a high accuracy for registering the livers. The sizes and positions of the ablation area and tumor are accurately compared for the assessment of ablation surgery.