In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However, this approach suffers from low efficiency and poor accuracy. With the development of deep learning, automatic liver tumor segmentation algorithms based on neural networks have emerged, for the improvement of the work efficiency. However, existing liver tumor segmentation algorithms still have several limitations: (1) they often encounter the common issue of class imbalance in liver tumor segmentation tasks, where the tumor region is significantly smaller than the normal tissue region, causing models to predict more negative samples and neglect the tumor region; (2) they fail to adequately consider feature fusion between global contexts, leading to the loss of crucial information; (3) they exhibit weak perception of local details such as fuzzy boundaries, irregular shapes, and small lesions, thereby failing to capture important features. To address these issues, we propose a Multi-Axis Attention Conditional Generative Adversarial Network, referred to as MA-cGAN. Firstly, we propose the Multi-Axis attention mechanism (MA) that projects three-dimensional CT images along different axes to extract two-dimensional features. The features from different axes are then fused by using learnable factors to capture key information from different directions. Secondly, the MA is incorporated into a U-shaped segmentation network as the generator to enhance its ability to extract detailed features. Thirdly, a conditional generative adversarial network is built by combining a discriminator and a generator to enhance the stability and accuracy of the generator’s segmentation results. The MA-cGAN was trained and tested on the LiTS public dataset for the liver and tumor segmentation challenge. Experimental results show that MA-cGAN improves the Dice coefficient, Hausdorff distance, average surface distance, and other metrics compared to the state-of-the-art segmentation models. The segmented liver and tumor models have clear edges, fewer false positive regions, and are closer to the true labels, which plays an active role in medical adjuvant therapy. The source code with our proposed model are available at https://github.com/jhliao0525/MA-cGAN.git.

In Cone Beam Computed Tomography (CBCT) images, accurate tooth segmentation is crucial for oral health, providing essential guidance for dental procedures such as implant placement and difficult tooth extractions (impactions). However, due to the lack of a substantial amount of dental data and the complexity of tooth morphology in CBCT images, the task of tooth segmentation faces significant challenges. This may lead to issues such as overfitting and training instability in existing algorithms, resulting in poor model generalization. Ultimately, this may impact the accuracy of segmentation results and could even provide incorrect diagnostic and treatment information. In response to these challenges, we introduce PPA-SAM, an innovative dual-encoder segmentation network that merges the currently popular Segment Anything Model (SAM) with the 3D medical segmentation network, VNet. Through the use of adapters, we achieve parameter reuse and fine-tuning, enhancing the model’s adaptability to specific CBCT datasets. Simultaneously, we utilize a three-layer convolutional network as both a discriminator and a generator for adversarial training. The PPA-SAM model seamlessly integrates the high-precision segmentation performance of convolutional networks with the outstanding generalization capabilities of SAM models, achieving more accurate and robust three-dimensional tooth segmentation in CBCT images. Evaluation of a small CBCT dataset demonstrates that PPA-SAM outperforms other networks in terms of accuracy and robustness, providing a reliable and efficient solution for three-dimensional tooth segmentation in CBCT images. This research has a positive impact on the management of dentofacial conditions from oral implantology to orthognathic surgery, offering dependable technological support for future oral diagnostics and treatment planning.