The field of medical image segmentation and reconstruction is rapidly advancing with the development of new techniques and models. A key direction in this field is the use of diffusion models, which have shown impressive results in image processing tasks. These models are being used to improve the accuracy and efficiency of medical image segmentation, particularly in semi-supervised and limited-angle reconstruction scenarios. Another area of focus is the integration of clinical metadata and prior information into the segmentation process, which has been shown to improve reconstruction fidelity and reduce the need for annotated training data. Additionally, researchers are exploring the use of multi-task learning and auxiliary data to improve vessel segmentation and other related tasks. Noteworthy papers in this area include MetaSSL, which proposes a universal framework for semi-supervised medical image segmentation using a spatially heterogeneous loss function, and Prior-Guided Residual Diffusion, which presents a diffusion-based framework for calibrated and efficient medical image segmentation. CbLDM is also a notable work, which proposes a deep learning model for recovering nanostructure from pair distribution function. Clinical Metadata Guided Limited-Angle CT Image Reconstruction is another significant contribution, which proposes a two-stage diffusion framework guided by structured clinical metadata to improve reconstruction fidelity. Data-Dependent Smoothing for Protein Discovery with Walk-Jump Sampling is a valuable work that introduces a data-dependent smoothing walk-jump framework to account for the heterogeneous distribution of protein data. DCDB: Dynamic Conditional Dual Diffusion Bridge for Ill-posed Multi-Tasks is a notable paper that proposes a dynamic conditional double diffusion bridge training paradigm to build a general framework for ill-posed multi-tasks. Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training is a significant work that proposes a multi-task learning framework to segment vessels in liver MRI without contrast. Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation is another important contribution, which develops a semi-supervised medical image segmentation framework that effectively integrates spatial regularization methods and volume priors. Multi-Strategy Guided Diffusion via Sparse Masking Temporal Reweighting Distribution Correction is a valuable work that proposes a sparse condition temporal reweighted integrated distribution estimation guided diffusion model for sparse-view CT reconstruction.