Despite significant advancements in medical image registration, current techniques face limitations in accurately aligning images captured at different time points, particularly for lung imaging. Traditional registration methods are often laborintensive and computationally complex, while deep learning-based approaches, though faster, may fail to capture temporal biological changes such as nodule growth. To address this challenge, we proposes a weakly supervised registration method for lung CT images. In this method, a deep learning-based deformation field estimation mechanism is designed, which is built upon the VoxelMorph architecture. This mechanism can effectively estimate the deformation field that aligns images from different time points by learning the spatial relationships between lung regions. To further enhance the registration accuracy and ensure that the temporal biological changes are properly reflected, a novel growth consistency loss function is designed into the training process. This loss function not only ensures the similarity between the registered images but also captures the temporal growth patterns of lung nodules, thus providing a more accurate representation of the biological changes over time. Experimental results demonstrate that the proposed method achieves significant improvements in registration accuracy. These findings highlight the effectiveness of incorporating growth consistency into the registration framework and underscore the importance of balancing similarity and growth-related losses for optimal performance.

Chronic Obstructive Pulmonary Disease (COPD) severely affects people’s health. With this in mind, we propose a novel Multi-Stage Composite Ensemble Learning Framework (MSCELF) that can diagnose COPD without utilising pulmonary function tests data. Our method explores 12 features from the patients’ baseline data, medical history, blood tests, and arterial blood gas analysis. In the first stage of our approach, three different ensemble learning methods are employed. The second stage involves the utilization of two machine learning methods. Finally, the Murphy’s method is integrated in the final stage to combine the outputs, with weights being assigned based on their information quantity and credibility. We evaluate our method on a clinical dataset of 329 patients and show that it outperforms existing methods in terms of accuracy, AUC, sensitivity, specificity, PPV, NPV, and F1 score, which are 0.7980, 0.8082, 0.8551, 0.6835, 0.8570, 0.6531, 0.8560.

Medical imaging research has seen advances in deep learning, but temporal aspects in time-series medical imaging data are often overlooked, leading to diagnostic limitations. This study proposes a spatial-temporal fusion approach for medical image segmentation by integrating a 3D UNet spatial network with a novel temporal network, DTransformer, capable of handling irregularly spaced sequences. The 3D UNet extracts spatial features, while DTransformer processes temporal information with time distance considerations using a novel self-attention mechanism. Experiments on a lung CT dataset show significant segmentation accuracy improvements with the fusion approach. DTransformer proves effective for unequally spaced sequences and boosts performance. And spatial-temporal fusion enhances medical image segmentation. Moveover, DTransformer's ability to manage temporal context and time distance holds promise for various tasks, indicating a new avenue for research.

Soft tissues sarcomas include striated muscle, fibrous tissue, fat, and other soft tissues. Simultaneously, their mortality rates are comparable to those of esophageal cancer, cervical cancer, and other cancers. Prior to surgical resection of patients, it is frequently necessary to study and diagnose the sarcoma area using MRI images in order to design a better surgical plan. However, artificial approaches for diagnosing the sarcoma region are time-consuming and error-prone. While the advent of artificial intelligence allows for computer-assisted diagnosis of the sarcoma region. Nevertheless, there is currently a scarcity of high-quality soft tissue sarcoma imaging data sets in relevant sectors. Thus, with the aim to investigate how to use multi-modal MRI images of patients with soft tissue sarcomas to segment the sarcoma area, we collect and process 15372 multi-modal MRI images in coronal of 40 patients with soft tissue sarcomas found in the thigh, which we subsequently combine with the help of several clinicians to mark the sarcoma area. The multi-modal MRI imaging data set of soft tissue sarcoma is therefore acquired by a number of preprocessing techniques. The sarcoma area is then segmented using a multi-encoder and single-decoder network that adapts to multiple input modalities. For motivating the network to learn the important semantic features of different modalities, we design a feature fusion strategy mechanism that is applied to the skip connection. Additionally, self-supervised learning is being investigated to address the issue of a small number of data points in the data set. Experiments show that our network can achieve the highest Dice score of 57.76% on our data set. Our code and the dataset are available at https://github.com/syaxx0819/The-Multimodal-Soft-Tissue-Sarcoma-Image-Dataset.

In order to better explore the evolution process of lung nodules in lung cancer patients, we collect lung CT data at multiple time points of lung cancer patients, track and mark the CT positions of the same lung nodules in lung cancer patients at different time points, and make time-series CT data sets of lung cancer patients. After that, 3D-UNet model is used to detect lung nodules on our data set. Experiment proves the effectiveness and availability of the data set, and also proved that the image data at multiple time points could improve the accuracy of the model’s identification of lung nodules.