Semisupervised 3D segmentation of pancreatic tumors in positron emission tomography/computed tomography images using a mutual information minimization and cross-fusion strategy.

Background: Accurate segmentation of pancreatic cancer tumors using positron emission tomography/computed tomography (PET/CT) multimodal images is crucial for clinical diagnosis and prognosis evaluation. However, deep learning methods for automated medical image segmentation require a substantial amount of manually labeled data, making it time-consuming and labor-intensive. Moreover, addition or simple stitching of multimodal images leads to redundant information, failing to fully exploit the complementary information of multimodal images. Therefore, we developed a semisupervised multimodal network that leverages limited labeled samples and introduces a cross-fusion and mutual information minimization (MIM) strategy for PET/CT 3D segmentation of pancreatic tumors. Methods: Our approach combined a cross multimodal fusion (CMF) module with a cross-attention mechanism. The complementary multimodal features were fused to form a multifeature set to enhance the effectiveness of feature extraction while preserving specific features of each modal image. In addition, we designed an MIM module to mitigate redundant high-level modal information and compute the latent loss of PET and CT. Finally, our method employed the uncertainty-aware mean teacher semi-supervised framework to segment regions of interest from PET/CT images using a small amount of labeled data and a large amount of unlabeled data. Results: We evaluated our combined MIM and CMF semisupervised segmentation network (MIM-CMFNet) on a private dataset of pancreatic cancer, yielding an average Dice coefficient of 73.14%, an average Jaccard index score of 60.56%, and an average 95% Hausdorff distance (95HD) of 6.30 mm. In addition, to verify the broad applicability of our method, we used a public dataset of head and neck cancer, yielding an average Dice coefficient of 68.71%, an average Jaccard index score of 57.72%, and an average 95HD of 7.88 mm. Conclusions: The experimental results demonstrate the superiority of our MIM-CMFNet over existing semisupervised techniques. Our approach can achieve a performance similar to that of fully supervised segmentation methods while significantly reducing the data annotation cost by 80%, suggesting it is highly practicable for clinical application.

Burst of hopping trafficking correlated reversible dynamic interactions between lipid droplets and mitochondria under starvation.

Dynamic membrane contacts between lipid droplets (LDs) and mitochondria play key roles in lipid metabolism and energy homeostasis. Understanding the dynamics of LDs under energy stimulation is thereby crucial to disclosing the metabolic mechanism. Here, the reversible interactions between LDs and mitochondria are tracked in real-time using a robust LDs-specific fluorescent probe (LDs-Tags). Through tracking the dynamics of LDs at the single-particle level, spatiotemporal heterogeneity is revealed. LDs in starved cells communicate and integrate their activities (i.e., lipid exchange) through a membrane contact site-mediated mechanism. Thus the diffusion is intermittently alternated between active and confined states. Statistical analysis shows that the translocation of LDs in response to starvation stress is non-Gaussian, and obeys nonergodic-like behavior. These results provide deep understanding of the anomalous diffusion of LDs in living cells, and also afford guidance for rationally designing efficient transporter.