Computer-aided diagnosis (CAD) of pituitary microadenoma (PM) can assist doctors in decision-making, leading to improved lesion detection rates and diagnostic accuracy. However, the performance of existing CAD methods for PM detection has been hindered by the difficulty in obtaining high-quality segmentation results. This is primarily due to the small size of PM lesions and the relatively low resolution of Magnetic Resonance Imaging (MRI) images. To address these challenges, this paper proposes a new medical image detection and segmentation model based on spatio-temporal information. The proposed model aims to addresses the disease classification of PM by designing a network module based on multi-scale feature fusion. This module ensures comprehensive extraction of target semantic information while retaining clear spatial information, achieving classification from dynamic contrast-enhanced MRI(DCE-MRI) to identify positive PM samples. For the lesion segmentation of PM, after ROI Align alignment, the model further adds a semantic segmentation module named Dual-path Semantic Segmentation Module (DSSM) behind the mask head and classification head. This module captures more precise spatio-temporal semantic information, reducing accuracy loss and achieving pituitary segmentation. Finally, leveraging the results of pituitary detection, a feature pyramid network (FPN) layer is redesigned named Reuse Underlying Information Module (RUIM) to reuse low-level information, enhancing the detection capability for PM and thus achieving precise object detection and segmentation. The proposed model achieves an accuracy of 97.10% for PM, mAP of 50.24%, which is superior to multiple representative deep models for medical data. The code is available at https://github.com/BUCT-IUSRC/Research__PM-CAD .

The spatiotemporal prediction of on-orbit satellites is crucial for intention understanding and ensuring the successful completion of missions. Current spatiotemporal prediction methods primarily use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process sequential observation images to predict future states. However, these methods often result in poor prediction performance due to the network’s inherent limited ability to express complex details. In this work, a residual spatiotemporal diffusion (RSTD) model is proposed to learn the spatial and temporal characteristics of targets from spaceborne imaging sequences. Leveraging a historical database, the model utilizes the patterns of image feature changes to assist in predicting target shape variations during the next observation period. A spatiotemporal perception module, capable of capturing long-term dependencies, is incorporated into the denoising process, thereby endowing it with forecasting capabilities. Furthermore, by incorporating a residual dual-stream structure, the model separates the prediction of the target’s overall shape and dynamic changes, thus overcoming the issue of overly smooth predicted images. Comprehensive experiments demonstrate that the proposed method achieves a peak signal-to-noise ratio (PSNR) of about 35 dB during uniform and uniformly variable motion. It also outperforms existing methods in subsequent feature extraction and attitude estimation, supporting the spatiotemporal attitude prediction of on-orbit satellites.

The automatic segmentation of anterior mediastinal lesions in enhanced CT imaging is of significant importance in clinical diagnostics. Anterior mediastinal lesions are characterized by various types and blurred boundary, which increases the difficulty of anterior mediastinal lesions segmentation. This study leverages the robust zero-shot classification capability and semantic expression of CLIP to formulate CLIP-prompt that express the semantic correlation between images and text, so that CLIP-prompt guided cross-attention has been proposed. By integrating the CLIP-prompt into the image features through cross-attention, the network can focus more intently on the lesion areas. Additionally, to better capture the unknown categorical features of the images, this paper introduces a learnable image prompt that works in conjunction with an attention module integrated with textual information, thereby enhancing the constraints on the segmentation targets. Finally, to address the blurred boundary of the anterior mediastinal lesion, this study proposes a boundary-enhanced loss. By augmenting the weights of difficult-to-segment edge points, the network is enabled to focus on these challenging boundary areas, consequently improving the segmentation accuracy of these points. Compared to existing state-of-the-art methods, our approach has achieved an overall Dice coefficient of 89.43% and has achieved good performance in terms of ASSD metric for segmentation edges.

Chest computed tomography (CT) scans of coronavirus 2019 (COVID-19) disease usually come from multiple datasets gathered from different medical centers, and these images are sampled using different acquisition protocols. While integrating multicenter datasets increases sample size, it suffers from inter-center heterogeneity. To address this issue, we propose an augmented multicenter graph convolutional network (AM-GCN) to diagnose COVID-19 with steps as follows. First, we use a 3-D convolutional neural network to extract features from the initial CT scans, where a ghost module and a multitask framework are integrated to improve the network's performance. Second, we exploit the extracted features to construct a multicenter graph, which considers the intercenter heterogeneity and the disease status of training samples. Third, we propose an augmentation mechanism to augment training samples which forms an augmented multicenter graph. Finally, the diagnosis results are obtained by inputting the augmented multi-center graph into GCN. Based on 2223 COVID-19 subjects and 2221 normal controls from seven medical centers, our method has achieved a mean accuracy of 97.76%. The code for our model is made publicly.1

Automatic gastric tumor segmentation and lymph node (LN) classification not only can assist radiologists in reading images, but also provide image-guided clinical diagnosis and improve diagnosis accuracy. However, due to the inhomogeneous intensity distribution of gastric tumor and LN in CT scans, the ambiguous/missing boundaries, and highly variable shapes of gastric tumor, it is quite challenging to develop an automatic solution. To comprehensively address these challenges, we propose a novel 3D multi-attention guided multi-task learning network for simultaneous gastric tumor segmentation and LN classification, which makes full use of the complementary information extracted from different dimensions, scales, and tasks. Specifically, we tackle task correlation and heterogeneity with the convolutional neural network consisting of scale-aware attention-guided shared feature learning for refined and universal multi-scale features, and task-aware attention-guided feature learning for task-specific discriminative features. This shared feature learning is equipped with two types of scale-aware attention (visual attention and adaptive spatial attention) and two stage-wise deep supervision paths. The task-aware attention-guided feature learning comprises a segmentation-aware attention module and a classification-aware attention module. The proposed 3D multi-task learning network can balance all tasks by combining segmentation and classification loss functions with weight uncertainty. We evaluate our model on an in-house CT images dataset collected from three medical centers. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms, and obtains promising performance for tumor segmentation and LN classification. Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge. Our implementation is released at https://github.com/infinite-tao/MA-MTLN.