Superpixels are widely used in computer vision applications, as they conserve the running costs of subsequent processing while preserving the original performance. In most of the existing algorithms, the boundary adherence and the compactness of superpixels are necessarily inter-inhibitive because the color/gradient information is balanced against the position constraints, and the set criteria define all pixels indiscriminately. In this paper, we present a two-phase superpixel segmentation method based on the watershed transformation. After designing a new approach for calculating the flooding priority, we propose a new strategy with two distinct criteria for global and local refinement of the boundary pixels. These criteria reduce the compromise between the boundary adherence and compactness. Unlike the indiscriminate standards, our method applies different treatments to pixels in different environments, preserving the color homogeneity in content-rich areas while improving the regularity of the superpixels in content-plain regions. The superior accuracy and computing time of our proposed method are verified in comparison experiments with several state-of-the-art methods.

Superpixels are widely used in computer vision applications. Most of the existing superpixel methods use established criteria to indiscriminately process all pixels, resulting in superpixel boundary adherence and regularity being unnecessarily inter-inhibitive. This study builds upon a previous work by proposing a new segmentation strategy that classifies image content into meaningful areas containing object boundaries and meaningless parts that include color-homogeneous and texture-rich regions. Based on this classification, we design two distinct criteria to process the pixels in different environments to achieve highly accurate superpixels in content-meaningful areas and keep the regularity of the superpixels in content-meaningless regions. Additionally, we add a group of weights when adopting the color feature, successfully reducing the undersegmentation error. The superior accuracy and the moderate compactness achieved by the proposed method in comparative experiments with several state-of-the-art methods indicate that the content-adaptive criteria efficiently reduce the compromise between boundary adherence and compactness.

Lung cancer is the leading cause of cancer-related mortality, with small cell lung cancer and non-small cell lung cancer being the primary subtypes that exhibit distinct treatment approaches and prognostic outcomes. Accurate identification of these lung cancer classes holds significant importance in clinical practice. This study introduces an experience-driven interpretable multi-task network to concurrently perform segmentation and classification of small cell and non-small cell lung cancer. The core architecture of this multi-task model is based on StarNet, featuring a shared feature extraction branch and task-specific decoding branches for tumor segmentation and classification. Leveraging clinical knowledge of small cell lung cancer characteristics, such as indistinct edges, tissue invasion, and limited large cavity areas, two auxiliary branches are proposed: edge uncertainty estimation and tumor core area reconstruction. The values from edge uncertainty estimation and reconstruction integrity estimation are utilized in the classification branch to facilitate small cell lung cancer classification. Furthermore, for enhanced interpretability, bottleneck layer features are extracted for comparative learning, and a three-level contrastive loss is proposed to improve the differentiation of disease features. Lastly, an interpretable strategy based on trained feature query matching is presented, providing radiologists with clinical insights and reference images while the model outputs recognition predictions. Experimental results on the public dataset demonstrate that the proposed multi-task model not only outperforms single-task models but also offers a certain level of interpretability, thus enhancing radiologists' clinical decision-making processes.