Data economics drives AI by optimizing data usage, reducing costs, and enhancing efficiency. In 3D tumor segmentation, efficiency is crucial due to the high demand for labor-intensive manual annotations. Box-supervised segmentation offers a promising alternative but is constrained by tumor morphology complexity and boundary ambiguity. In this paper, we propose a novel 3D tumor segmentation model that integrates both positional and embedding features to facilitate inter-task collaboration. We introduce an Anatomical-Driven Class Activation Map to predefine the complex tumor morphology prior, which is further refined by our Geometric Pixel Co-embedding Learner. This learner utilizes contrastive learning to encode semantic information between center and edge pixels, enhancing pixel clustering and progressively refining tumor boundary segmentation in a coarse-to-fine manner. Our approach outperforms existing box-supervised methods in segmentation performance, with extensive experiments on four tumor datasets demonstrating significant improvements. This work provides a cost-effective and efficient solution for tumor segmentation, advancing the application of data economics in medical imaging.

Deep learning-based tumor segmentation methods typically require precise pixel-level annotations, which are costly in clinical practice. While bounding box supervision offers a more efficient alternative, existing approaches assume unrealistically tight box annotations, leading to performance degradation when applied to the loose boxes commonly produced by medical annotators. To address this challenge, we propose LooBox, a novel 3D segmentation framework that utilizes loose box annotations through a self-correction and bidirectional rectification paradigm. For the self-correction part, we propose a noise cleaner that comprehensively utilizes deterministic outer box information by integrating three complementary perspectives for predictive self-rectification: entropy mapping, gradient monitoring, and foreground-background affinity measurement. For the bidirectional rectification part, we introduce an augmentation-driven comprehensive consistency constraint strategy. Specifically, the framework incorporates: an asymmetric co-teaching architecture comprising a basic UNet and an enhanced UNet variant with a noise adapter, and an augmentation-driven consistency mechanism that computes pairwise loss between self-corrected predictions after each training iteration to ensure robust tumor feature extraction. Comprehensive evaluations on LIDC-IDRI, MSD-Lung, and MSD-Pancreas datasets demonstrate that LooBox achieves superior segmentation accuracy compared to state-of-the-art box-supervised methods.

Thoracic computed tomography (CT) currently plays the primary role in pulmonary nodule detection, where the reconstruction kernel significantly impacts performance in computer-aided pulmonary nodule detectors. The issue of kernel selection affecting performance has been overlooked in pulmonary nodule detection. This paper first introduces a novel pulmonary nodule detection dataset named Reconstruction Kernel Imaging for Pulmonary Nodule Detection (RKPN) for quantifying algorithm differences between the two imaging types. The dataset contains pairs of images taken from the same patient on the same date, featuring both smooth (B31f) and sharp kernel (B60f) reconstructions. All other imaging parameters and pulmonary nodule labels remain entirely consistent across these pairs. Extensive quantification reveals mainstream detectors perform better on smooth kernel imaging than on sharp kernel imaging. To address suboptimal detection on the sharp kernel imaging, we further propose an image conversion-based pulmonary nodule detector called ICNoduleNet. A lightweight 3D slice-channel converter (LSCC) module is introduced to convert sharp kernel images into smooth kernel images, which can sufficiently learn inter-slice and inter-channel feature information while avoiding introducing excessive parameters. We conduct thorough experiments that validate the effectiveness of ICNoduleNet, it takes sharp kernel images as input and can achieve comparable or even superior detection performance to the baseline that uses the smooth kernel images. The evaluation shows promising results and proves the effectiveness of ICNoduleNet.