A Lightweight Multi-Attention Deep Architecture for Liver Tumor Segmentation with Limited Samples

Abstract

Liver tumor segmentation from CT images is of paramount importance in medical image analysis. Accurate segmentation of liver tumor is crucial for effective diagnosis and treatment planning in hepatocellular carcinoma and other liver malignancies. Manual as well as traditional segmentation approaches often struggle with the complex and heterogeneous nature of liver tumors, necessitating advanced deep learning techniques. In this regard, the thesis introduces a supervised lightweight multi-attention deep architecture, termed as LiMAU, for liver tumor segmentation. It judiciously integrates the merits of an enhanced U-Net architecture, known as U-Net3+, traditional attention gates, and the convolutional block attention module (CBAM). The U-Net3+ represents a refined version of the traditional U-Net design, enriching it with full-scale skip connections and deep supervision, thereby enhancing its architectural sophistication. The full-scale skip connections merge low-level details with high-level semantics from feature maps at different scales, while deep supervision learns hierarchical representations from the fully aggregated feature maps. This structure is particularly beneficial for organs appearing at varying scales. The incorporation of U-Net3+ in the proposed LiMAU reduces the number of network parameters, thereby enhancing computational efficiency. The integration of traditional attention gates allows the proposed supervised model to selectively focus on relevant regions, enhancing feature learning by suppressing irrelevant background noise. On the other hand, the CBAM, which sequentially applies channel and spatial attention, further refines this focus by enhancing the model’s ability to capture contextual and fine-grained details essential for precise tumor delineation. The proposed LiMAU features batch normalization layers in each double convolution block, which leads to higher segmentation accuracy. Next, the thesis introduces a deep framework for semi-supervised learning as a promising solution for liver tumor segmentation with limited labeled samples. The proposed LiMAU serves as the cornerstone of the proposed semi-supervised approach. It integrates a novel adversarial consistency learning architecture, which effectively utilizes less labeled data while providing high segmentation accuracy. The proposed semi-supervised framework harnesses both labeled and unlabeled data to mitigate the requirement for extensive annotated data. The proposed framework judiciously integrates deep adversarial networks and the Π model. The Π model is based on the concept of consistency learning, which maintains the consistency of segmentation output during training across various random perturbations of both labeled and unlabeled data. The deep adversarial network consists of a segmentation network (SN) and two evaluation networks (ENs). While the SN is used for the segmentation task, the ENs are used to assess segmentation quality. The proposed LiMAU is used as the SN, while a variant of VGG16 is used for both ENs. During training, the first EN is incentivized to differentiate between annotated and unannotated image segmentation, the second one 3 is encouraged to distinguish between perturbed and unperturbed data, while the SN is encouraged to produce segmentations for unlabeled images similar to those for annotated ones. The performance of the proposed supervised and semi-supervised models is evaluated on two benchmark data sets, namely, MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS17) data and MICCAI-SLiver07 data, and compared with that of several state-of-the-art approaches. Experimental results demonstrate a significant improvement in segmentation accuracy over baseline models, with higher Dice similarity coefficients. This indicates that the combined use of traditional attention mechanisms and CBAM in the U-Net3+ architecture in supervised implementation as well as the semi-supervised adversarial network implementation significantly enhances the model’s ability to manage the variability and complexity of liver tumor morphology. These findings suggest that the proposed models hold great potential for clinical applications, offering improved precision in liver tumor segmentation.