Given the rich and complementary information contained in multi-phase CT images (CTs), they play an indispensable role in liver cancer diagnosis and prognosis, wherein an important prerequisite is liver tumor segmentation. However, spatial misalignments across phases and the limited availability of high-quality multi-phase CT datasets significantly hinder the performance of liver tumor segmentation. To tackle these challenges, we here propose SynLiTS, a novel multi-phase liver tumor segmentation framework. Its core idea is to synthesize multi-phase CTs with strictly-aligned liver tumors based on pseudo-normal multi-phase CTs. Specifically, an FFC-based Inpainter is first designed to generate pseudo-normal CTs by reconstructing dilated liver tumors. The pseudo-normal CTs and randomly generated tumor masks are then combined via a phase prompting-driven diffusion model to synthesize multi-phase liver tumor CTs with diverse tumor characteristics. In this way, multi-phase CTs with perfectly-aligned liver tumor labels are obtained. We also construct a real multi-phase liver tumor dataset, named MPLiTS. Finally, the synthesized and real multi-phase CTs are used to train a liver tumor segmentation model, which incorporates a context-aware fusion module to effectively learn and integrate multi-phase information. SynLiTS is evaluated on both internal and external datasets, and the results show that it outperforms state-of-the-art methods by large margins. Code will be released at https://github.com/Chyiun/SynLiTS.

Weakly-supervised segmentation (WSS) has emerged as a solution to mitigate the conflict between annotation cost and model performance by adopting sparse annotation formats (e.g., point, scribble, block, etc.). Typical approaches attempt to exploit anatomy and topology priors to directly expand sparse annotations into pseudo-labels. However, due to lack of attention to the ambiguous boundaries in medical images and insufficient exploration of sparse supervision, existing approaches tend to generate erroneous and overconfident pseudo proposals in noisy regions, leading to cumulative model error and performance degradation. In this work, we propose a novel WSS approach, named ProCNS, encompassing two synergistic modules devised with the principles of progressive prototype calibration and noise suppression. Specifically, we design a Prototype-based Regional Spatial Affinity (PRSA) loss to maximize the pair-wise affinities between spatial and semantic elements, providing our model of interest with more reliable guidance. The affinities are derived from the input images and the prototype-refined predictions. Meanwhile, we propose an Adaptive Noise Perception and Masking (ANPM) module to obtain more enriched and representative prototype representations, which adaptively identifies and masks noisy regions within the pseudo proposals, reducing potential erroneous interference during prototype computation. Furthermore, we generate specialized soft pseudo-labels for the noisy regions identified by ANPM, providing supplementary supervision. Extensive experiments on six medical image segmentation tasks involving different modalities demonstrate that the proposed framework significantly outperforms representative state-of-the-art methods.

Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-center data, especially in the face of significant data heterogeneity, notably in medical contexts. In the realm of medical image segmentation, the growing imperative to curtail annotation costs has amplified the importance of weakly-supervised techniques which utilize sparse annotations such as points, scribbles, etc. A pragmatic FL paradigm shall accommodate diverse annotation formats across different sites, which research topic remains under-investigated. In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation. In FedLPPA, a learnable universal knowledge prompt is maintained, complemented by multiple learnable personalized data distribution prompts and prompts representing the supervision sparsity. Integrated with sample features through a dual-attention mechanism, those prompts empower each local task decoder to adeptly adjust to both the local distribution and the supervision form. Concurrently, a dual-decoder strategy, predicated on prompt similarity, is introduced for enhancing the generation of pseudo-labels in weakly-supervised learning, alleviating overfitting and noise accumulation inherent to local data, while an adaptable aggregation method is employed to customize the task decoder on a parameter-wise basis. Extensive experiments on four distinct medical image segmentation tasks involving different modalities underscore the superiority of FedLPPA, with its efficacy closely parallels that of fully supervised centralized training. Our code and data will be available at https://github.com/llmir/FedLPPA.

Source camera identification is the process of discerning which camera has been used to capture a particular image. In our previous work, we tackled the problem with a vector of thirty-six features to train and test the classifier. The features include the lens aberration parameters and statistical measurements from pixel intensities. In this paper, we focus on reducing the feature set by stepwise discriminant analysis. Simulation is carried out to evaluate the classifier's performance by using the full feature set, reduced feature sets and randomly selected feature sets. The results show that the reduced feature sets can decrease the processing time while also maintain or even improve the classification accuracy under some circumstances.

Digital photograph stitching blends multiple images to form a single one with a wide field of view. However, artifacts may arise, often due to photometric inconsistency and geometric misalignment among the images. Several existing techniques tackle this problem by methods such as pixel selection or pixel blending, which involve the matching and adjustment of intensity, frequency, and gradient values. However, our experience indicates that these methods have yet fully incorporated the uniformity properties of the photometric inconsistency. In this paper, we first explain the causes of inconsistency and its uniformity property. Then, by mathematical analysis, we show that the matching on the intensity and even the gradient domain is insufficient for some non-uniform inconsistencies. Our method thus adds the extra requirement of an optimal matching of curvature. We then explain how its variables can affect the computational and visual performance. Simulations are carried out using our method, with some masks designed with these two concerns. Some real examples show that our method can produce pleasant visual results even when both misalignment and non-uniform inconsistency exist.