Automatic brain lesion segmentation enhances diagnostic efficiency by enabling detailed texture analysis and precise delineation of tumor subregions. Multimodal MRI has improved segmentation accuracy by combining complementary information from different modalities. Conventional methods either fuse all modalities uniformly, obscuring how individual modalities contribute to specific segmentation subtasks, or predefine modality-to-subregion mappings based on prior medical knowledge. The former limits interpretability on modality contribution during training, while the latter relies on parameter-heavy architectures like cascaded subnetworks, making models struggle to adapt to varying modalities. To address these challenges, this paper proposes SwitchNet, a novel model that integrates interpretability into the training process and optimizes parameter efficiency without relying on predefined modality selection. First, we propose Adaptive Encoder and Decoder Blocks employing dynamic switching mechanisms to efficiently allocate feature space and prioritize critical subtasks. These blocks enable the model to automatically identify and utilize the most informative modalities. By strategically allocating parameters to modalities, our model optimizes overall parameter complexity while maintaining strong performance. Second, we propose a Guide-Contribution Mechanism to provide interpretability during training by quantitatively revealing the contributions of individual modalities to the segmentation process. This mechanism offers valuable insights into how the model delineates tumor subregions. SwitchNet was validated on three benchmark datasets, including BraTS 2023, ISLES 2022, and UCSF-PDGM, achieving competitive segmentation performance while significantly enhancing interpretability and maintaining parameter efficiency without extra cost. These results highlight its potential for efficient tumor segmentation and clinical explainability.

Neural-like P systems are membrane computing models inspired by natural computing. Spiking neurral (SN) P systems, a kind of neural-like P systems, are viewed as third-generation neural network models. Although real neurons have complex structures, classical SN P systems simplify the structures and corresponding mechanisms to stationary two-dimensional graphs and lack related evolution mechanisms on spikes and channels, which limits the real applications of these models. In this paper, we propose a new hybrid SN P system with evolutionary channels (HN P systems), including three new types of rules for dynamically generating or removing one-one and one-many/many-one channels with related evolutions of spikes on the hybrid neuron structures. Two dynamic regulatory factors are also presented on rules to help guide the optimization of the HN P systems automatically. Based on the new P system, a multiple brain metastases (BMs) segmentation model is developed. The experimental results indicate that the proposed models outperform the state-of-the-art methods on the BMs, which have large variations in sizes, positions and shapes, and low contrast with their surroundings. Performances on the head and neck segmentation dataset also verifies the effectiveness of the HN P system.

Neural-like P systems are membrane computing models inspired by natural computing and are viewed as third-generation neural network models. Although real neurons have complex structures, classical neural-like P systems simplify the structures and corresponding mechanisms to two-dimensional graphs or tree-based firing and forgetting communications, which limit the real applications of these models. In this paper, we propose a hypergraph-based numerical neural-like (HNN) P system containing five types of neurons to describe the high-order correlations among neuron structures. Three new kinds of communication mechanisms among neurons are also proposed to address numerical variables and functions. Based on the new neural-like P system, a tumor/organ segmentation model for medical images is developed. The experimental results indicate that the proposed models outperform the state-of-the-art methods based on two hippocampal datasets and a multiple brain metastases dataset, thus verifying the effectiveness of the HNN P system in correctly segmenting tumors/organs.

In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness. Since there is a rigorous mapping between the Signed Distance Map (SDM) calculated from object boundary contours and the binary segmentation map, we exploit the feasibility of learning the SDM directly from medical scans. By converting the segmentation task into predicting an SDM, we show that our proposed method retains superior segmentation performance and has better smoothness and continuity in shape. To leverage the complementary information in traditional segmentation training, we introduce an approximated Heaviside function to train the model by predicting SDMs and segmentation maps simultaneously. We validate our proposed models by conducting extensive experiments on a hippocampus segmentation dataset and the public MICCAI 2015 Head and Neck Auto Segmentation Challenge dataset with multiple organs. While our carefully designed backbone 3D segmentation network improves the Dice coefficient by more than 5% compared to current state-of-the-arts, the proposed model with SDM learning produces smoother segmentation results with smaller Hausdorff distance and average surface distance, thus proving the effectiveness of our method.

Multi-modal imaging plays a critical role in various clinical applications. However, due to the associated high cost and potential risk, the acquisition of multi-modal images could be limited. To address this issue, many cross-modality image synthesis methods have been proposed. The state-of-the-art methods are mainly based on traditional convolutional generative adversarial networks (GANs) for generating target images. In 3D medical image synthesis, an open problem is how to efficiently exploit the spatial correlations of the 3D image sequence to resolve the inter-slice discontinuity and unevenness artifacts. In this paper, we propose a novel Context aware Residual Recurrent Generative Adversarial Network (short for CorGAN) for sequential medical image generation, which jointly exploits the spatial dependencies of the sequences as well as the peer image generation with GANs. Experimental results show the robustness and accuracy of our method, which outperforms the-state-of-the-art methods in synthesizing target 3D images from the corresponding source images.