• MAF preserves low-level feature representations, while fusing global and local information to generate robust class-agnostic features. • TA2MP, as a unified feature transformation mechanism equipped with an automatic learnable prompt branch, reduces human reliance and disentangles domain- and class-specific information through contrastive learning. • UDG-Prom integrates the MAF and TA2MP modules to address the CD-FSS task with SAM. • Our model achieves competitive or superior performance compared to state-of-the-art methods on four CD-FSS benchmarks, and its strong generalization ability is comprehensively validated through evaluations on more difficult cross-domain datasets including CT-Lung (medical) and SUIM (underwater). Large Vision Models (LVMs), exemplified by SAM, contain powerful general knowledge from extensive pre-training, yet they often underperform in highly specialized domains. Building large models tailored for each domain is usually impractical due to the substantial cost of data collection and training. Therefore, a key challenge is how to tap into SAM’s strong knowledge base and transfer it effectively to new, domain-specific tasks, especially under Cross-Domain or Few-Shot constraints. Previous efforts have leveraged prior knowledge from foundation models for transfer learning; however, they typically target specific tasks and exhibit limited robustness in broader applications. To tackle this issue, we propose a Unified Dense-Guided Semantic Prompting framework (UDG-Prom), a new paradigm for Cross-Domain Few-Shot Segmentation (CD-FSS). First, a Multi-level Adaptation Framework (MAF) is used for integrated feature extraction as prior knowledge. Then, we incorporate a Task-Adaptive Auto Meta Prompt (TA 2 MP) module to enable the extraction of class-domain-agnostic features and generate high-quality, learnable visual prompts. By combining learnable prompts with a structured model and prototype disentanglement, this method retains SAM’s prior knowledge and effectively adapts to CD-FSS through category and domain cues. Extensive experiments on four benchmarks show that our model not only surpasses state-of-the-art CD-FSS approaches but also achieves a remarkable improvement in average accuracy.

This paper proposes a novel dehazing method termed Haze-Restoration Curve Model (HRCM), which transforms the single-image dehazing task into a specific curve estimation problem, achieving haze removal through an intuitive and simple nonlinear curve mapping. Unlike methods based on Atmospheric Scattering Model (ASM), HRCM does not require the computation of complex physical parameters. Instead, it estimates two intuitive curvature adjustment coefficients. Moreover, compared to recent end-to-end dehazing methods, HRCM circumvents the challenging modeling of static mapping functions, thereby improving the generalization ability and dehazing performance of the model. All of these are attributed to a meticulously designed dehazing curve, which first reversing the hazy image to highlight obscured regions, and then specifies a set of high-order functions to remap hazy pixels for image restoration. Moreover, to estimate the curve parameters, we designed a dual-branch Deep Dehaze Curve Estimation Network(DDCEN), which consists of the Residual Swin Transformer Block(RTSB) and the Large kernel convolutional Attention Block(LAB). Specifically, RTSB captures the global fog density distribution features of foggy images by introducing window self-attention and shifted window mechanisms, providing support for global semantic information for subsequent parameter estimation. LAB captures local multi-scale features by constructing a large receptive field, and uses the attention mechanism of feature pooling in horizontal and vertical directions to focus on detail regions, refining the local details of the parameter map. Extensive experiments on synthetic and real-world hazy image datasets demonstrate that the proposed approach achieves superior performance in terms of quantitative accuracy and subjective visual quality compared to the current state-of-the-art methods. The source code of our HRCM is available at https://github.com/larrylanrui/HRCM.

Solving jigsaw puzzles has been extensively studied. While most existing models focus on solving either small-scale puzzles or puzzles with no gap between fragments, solving large-scale puzzles with gaps presents distinctive challenges in both image understanding and combinatorial optimization. To tackle these challenges, we propose a framework of Evolutionary Reinforcement Learning with Multi-head Puzzle Perception (ERL-MPP) to derive a better set of swapping actions for solving the puzzles. Specifically, to tackle the challenges of perceiving the puzzle with gaps, a Multi-head Puzzle Perception Network (MPPN) with a shared encoder is designed, where multiple puzzlet heads comprehensively perceive the local assembly status, and a discriminator head provides a global assessment of the puzzle. To explore the large swapping action space efficiently, an Evolutionary Reinforcement Learning (EvoRL) agent is designed, where an actor recommends a set of suitable swapping actions from a large action space based on the perceived puzzle status, a critic updates the actor using the estimated rewards and the puzzle status, and an evaluator coupled with evolutionary strategies evolves the actions aligning with the historical assembly experience. The proposed ERL-MPP is comprehensively evaluated on the JPLEG-5 dataset with large gaps and the MIT dataset with large-scale puzzles. It significantly outperforms all state-of-the-art models on both datasets.

Distribution shift is a common challenge in medical images obtained from different clinical centers, significantly hindering the deployment of pre-trained semantic segmentation models in real-world applications across multiple domains. Continual Test-Time Adaptation (CTTA) has emerged as a promising approach to address cross-domain distribution shifts during continually evolving target domains. Most existing CTTA methods rely on incrementally updating model parameters, which inevitably suffer from error accumulation and catastrophic forgetting, especially in long-term adaptation. Recent prompt-tuning-based works have shown potential to mitigate the two issues above by updating only visual prompts. While these approaches have demonstrated promising performance, several limitations remain: 1) lacking multi-scale prompt diversity, 2) inadequate incorporation of instance-specific knowledge, and 3) risk of privacy leakage. To overcome these limitations, we propose Multi-scale Global-Instance Prompt Tuning (MGIPT), to enhance scale diversity of prompts as well as capture both globaland instance-level knowledge for robust CTTA. Specifically, MGIPT consists of an Adaptive-scale Instance Prompt (AIP) and a Multi-scale Global-level Prompt (MGP). AIP dynamically learns lightweight and instance-specific prompts to mitigate error accumulation with adaptive optimal-scale selection mechanism. MGP captures domain-level knowledge across different scales to ensure robust adaptation with anti-forgetting capabilities. These complementary components are combined through a weighted ensemble approach, enabling effective dual-level adaptation that integrates both global and local information. Extensive experiments on medical image segmentation benchmarks (five optic disc/cup datasets and four polyp datasets) demonstrate that our MGIPT outperforms state-of-the-art methods, achieving robust adaptation across continually changing target domains. Notably, our MGIPT exhibits particularly strong performance in longterm CTTA scenarios, showing great anti-forgetting ability.

Accurate 3D vascular segmentation is essential for diagnosing and treating vascular diseases. This task remains challenging due to the complexity of the 3D data and the morphological diversity of blood vessels. In recent years, state space models (SSMs) have received a great attention for its good performance while preserving global receptive field and consuming less computing resources and time. Inspired by this, we propose a model called Swin-VasMamba for 3D vascular segmentation. It consists of a network called CMU-Net and a topologically constrained loss function called dsh loss. We compare our model with several other advanced segmentation models based on CNN, Transformer and Mamba. The results show that Swin-VasMamba achieves a state-of-the-art performance, with the highest Dice coefficient of 0.880, the lowest 95th-percentile of Hausdorff Distance (HD95) of 0.673, and the lowest Average Surface Distance (ASD) of 0.159 on a benchmark dataset.

Puzzle solving has recently become a popular research topic. Existing solvers often overlook puzzles with missing pieces. The missing pieces, together with gaps between pieces, pose significant challenges, amplified by a large solution space. To tackle the challenges, we propose Co-Evolutionary Agents for Reassembling and Inpainting (CEARI), one agent to inpaint missing contents and the other to reassemble the puzzle, with a shared perception network to perceive the puzzle status. The reassembly agent utilizes an evolutionary algorithm to explore the large solution space, to discover a sequence of fragment-swapping actions to efficiently reassemble the puzzle, while the inpainting agent evolves from using a local outpainting network at the early stage to using a global inpainting network at the latter stage. Furthermore, a co-evolutionary training paradigm is designed to iteratively evolve the two agents in a coherent and collaborative manner, improving reassembly accuracy and inpainting quality simultaneously. Experimental results on three datasets show that CEARI largely outperforms state-of-the-art methods in terms of both reassembly accuracy and inpainting quality.

Automatic radiology report generation can alleviate the workload for physicians and minimize regional disparities in medical resources, therefore becoming an important topic in the medical image analysis field. It is a challenging task, as the computational model needs to mimic physicians to obtain information from multi-modal input data (i.e., medical images, clinical information, medical knowledge, etc.), and produce comprehensive and accurate reports. Recently, numerous works have emerged to address this issue using deep-learning-based methods, such as transformers, contrastive learning, and knowledge-base construction. This survey summarizes the key techniques developed in the most recent works and proposes a general workflow for deep-learning-based report generation with five main components, including multi-modality data acquisition, data preparation, feature learning, feature fusion and interaction, and report generation. The state-of-the-art methods for each of these components are highlighted. Additionally, we summarize the latest developments in large model-based methods and model explainability, along with public datasets, evaluation methods, current challenges, and future directions in this field. We have also conducted a quantitative comparison between different methods in the same experimental setting. This is the most up-to-date survey that focuses on multi-modality inputs and data fusion for radiology report generation. The aim is to provide comprehensive and rich information for researchers interested in automatic clinical report generation and medical image analysis, especially when using multimodal inputs, and to assist them in developing new algorithms to advance the field.

Video-based visible-infrared person re-identification (VVI-ReID) task focuses on cross-modality retrieval of pedestrian videos, which are captured in visible and infrared modalities by non-overlapping cameras across diverse scenes, and holds significant value for security surveillance scenarios. The challenges of this task mainly stem from three issues: the difficulty of capturing comprehensive spatio-temporal cues, intra-class variations within video sequences, and inter-modality discrepancies between visible and infrared data. Existing methods mainly try to address the modality gap or focus on one of the other aspects, but rarely do they jointly consider these key factors. Motivated by these core challenges, we propose the M3-ReID (Multi-View & Granularity & Modality) method, a unified framework that simultaneously enhances spatio-temporal feature extraction, intra-class discrimination, and cross-modality consistency. Specifically, to capture diverse spatio-temporal patterns, we design a Multi-View Learning module that leverages different spatial and temporal-spatial perspectives to adaptively emphasize diverse key regions and motion cues. To enhance intra-class modeling of each identity, we introduce a Multi-Granularity Representation strategy that optimizes features across both fine-grained frame level and coarse-grained video level by minimizing mutual information among redundant frames while enhancing identity representations. Furthermore, to bridge the visible-infrared gap, we propose a Multi-Modality Alignment mechanism that explicitly aligns metric learning and cross-modality matching goals, transforming features into a unified embedding space with modality consistency and class discrimination. Extensive experiments on benchmark VVI-ReID datasets demonstrate the superiority of our proposed M3-ReID framework against existing methods.

Drug-drug interaction (DDI) identification is a crucial aspect of pharmacology research. There are many DDI types (hundreds), and they are not evenly distributed with equal chance to occur. Some of the rarely occurred DDI types are often high risk and could be life-critical if overlooked, exemplifying the long-tailed distribution problem. Existing models falter against this distribution challenge and overlook the multi-faceted nature of drugs in DDI prediction. In this paper, a novel multi-modal deep learning-based framework, namely TFDM, is introduced to leverage multiple properties of a drug to achieve DDI classification. The proposed framework fuses multimodal features of drugs, including graph-based, molecular structure, Target and Enzyme, for DDI identification. To tackle the challenge posed by the distribution skewness across categories, a novel loss function called Tailed Focal Loss is introduced, aimed at further enhancing the model performance and address gradient vanishing problem of focal loss in extremely long-tailed dataset. Intensive experiments over 4 challenging long-tailed dataset demonstrate that the TFMD outperforms the most recent SOTA methods in long-tailed DDI classification tasks. The source code is released to reproduce our experiment results: https://github.com/IcurasLW/TFMD_Longtailed_DDI.git

The loss function plays an important role in deep learning models as it determines the model convergence behavior and performance. In semantic segmentation, many methods utilize pixel-wise (e.g. cross-entropy) and region-wise (e.g. dice) losses while boundary-wise loss is underexplored. It is known that one of the key aims of semantic segmentation is to precisely delineate objects' boundaries. Hence, it is essential to design a loss function that measures the errors around objects' boundaries. Fuzzy rough sets are constituted by the fuzzy equivalence relation, which is commonly used to measure the difference between two sets. In this paper, the lower approximation of fuzzy rough sets is proposed to construct the boundary-wise loss in deep learning models for the first time. The experiments with various segmentation models and datasets have verified that the proposed fuzzy rough sets loss is superior to other boundary-wise losses in terms of segmentation accuracy and time complexity. Compared with the commonly used pixel-wise and region-wise losses, the proposed boundary-wise loss performs similarly in dice coefficient, pixel-wise accuracy, but has a better performance in Hausdorff distance and symmetric surface distance. It indicates that the proposed loss provides a better guidance for segmentation models in producing more accurate shapes of the target objects.

Deep learning methods have achieved an excellent performance in medical image segmentation. However, the practical application of deep learning-based segmentation models is limited in clinical settings due to the lack of reliable information about the segmentation quality. In this article, we propose a novel quality control algorithm based on fuzzy uncertainty to quantify the quality of the predicted segmentation results as part of the model inference process. First, test-time augmentation and Monte Carlo dropout are applied simultaneously to capture both the data and model uncertainties of the trained image segmentation model. Then, a fuzzy set is generated to describe the captured uncertainty with the assistance of the linear Euclidean distance transform algorithm. Finally, the fuzziness of the generated fuzzy set is adopted to calculate an image-level segmentation uncertainty and, therefore, to infer the segmentation quality. Extensive experiments using five medical image segmentation applications on the detection of skin lesion, nuclei, lung, breast, and cell are conducted to evaluate the proposed algorithm. The experimental results show that the estimated image-level uncertainties using the proposed method have strong correlations with the segmentation qualities measured by the Dice coefficient, resulting in absolute Pearson correlation coefficients of 0.60–0.92. Our method outperforms other five state-of-the-art quality control methods in classifying the segmentation results into good and poor quality groups (area under the receiver operating curve of greater than 0.92, while other methods are below 0.85).

Deep convolutional neural networks (DCNN)-based methods have achieved promising performance in semantic image segmentation. However, in practical applications, it is important not only to produce the segmentation result but also to inform the segmentation quality (e.g. confidence of the segmentation result). In this paper, we propose to utilize fuzzy sets for estimating segmentation uncertainty, therefore to infer the quality of segmentation produced by a DCNN model. The proposed method combines test-time augmentation and fuzzy sets to estimate an image-level uncertainty. Six different fuzziness measures are implemented and compared, in order to select the best fuzzy uncertainty metric for the proposed method. A public skin lesion dataset is used to evaluate the method. The results show a strong correlation (Pearson correlation coefficient of 0.736) between our proposed uncertainty measure and image segmentation quality measured by Dice coefficient.

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the same organ. This is due to the significant intensity variations of different image modalities. In this paper, we propose a novel end-to-end deep neural network to achieve multi-modality image segmentation, where image labels of only one modality (source domain) are available for model training and the image labels for the other modality (target domain) are not available. In our method, a multi-resolution locally normalized gradient magnitude approach is firstly applied to images of both domains for minimizing the intensity discrepancy. Subsequently, a dual task encoder-decoder network including image segmentation and reconstruction is utilized to effectively adapt a segmentation network to the unlabeled target domain. Additionally, a shape constraint is imposed by leveraging adversarial learning. Finally, images from the target domain are segmented, as the network learns a consistent latent feature representation with shape awareness from both domains. We implement both 2D and 3D versions of our method, in which we evaluate CT and MRI images for kidney and cardiac tissue segmentation. For kidney, a public CT dataset (KiTS19, MICCAI 2019) and a local MRI dataset were utilized. The cardiac dataset was from the Multi-Modality Whole Heart Segmentation (MMWHS) challenge 2017. Experimental results reveal that our proposed method achieves significantly higher performance with a much lower model complexity in comparison with other state-of-the-art methods. More importantly, our method is also capable of producing superior segmentation results than other methods for images of an unseen target domain without model retraining. The code is available at GitHub (https://github.com/MinaJf/LMISA) to encourage method comparison and further research.

Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in many application areas, due to the capability of automatically extracting and aggregating spatial and channel-wise features from images. Most recent studies have concentrated on modifying convolutional kernel size to achieve multi-scale spatial information. In this paper, we introduce a novel fuzzy integral module to the CNNs for fusing the information across feature channels. The fuzzy integral is a mathematical aggregation operator and is widely used in decision level fusion. Herein, we utilize a special case of fuzzy integrals namely ordered weight averaging (OWA) to merge information at feature level. Three publicly available datasets were used to evaluate the proposed fuzzy CNN model for image segmentation. The results show that the proposed fuzzy module helps in reducing the baseline model parameters by 58.54% while producing higher segmentation accuracy (measured by Dice) than the baseline method and a similar method reported in the literature.

A novel ensemble feature selection (FS) framework using fuzzy aggregation is proposed in this paper. It consists of three main steps: distribution generation of feature importance, distribution ensemble using fuzzy aggregation, and defuzzification for feature ranking. Based on four state-of-the-art FS methods (named as base selectors in our algorithm) selected from different method categories, different fuzzy aggregation operators were implemented to achieve ensemble learning for decision making. A training data repository that consists of eight datasets was used for parameter tuning of the proposed framework. The proposed framework using drastic sum aggregation achieved the best performance and was subsequently evaluated on eight independent testing datasets. Remarkably, the proposed method achieved the best classification accuracy and the highest stability compared with the four base FS methods. It also outperformed our previously proposed score based ensemble method [1].

This paper presents a novel meta learning framework for feature selection (FS) based on fuzzy similarity. The proposed method aims to recommend the best FS method from four candidate FS methods for any given dataset. This is achieved by firstly constructing a large training data repository using data synthesis. Six meta features that represent the characteristics of the training dataset are then extracted. The best FS method for each of the training datasets is used as the meta label. Both the meta features and the corresponding meta labels are subsequently used to train a classification model using a fuzzy similarity measure based framework. Finally the trained model is used to recommend the most suitable FS method for a given unseen dataset. This proposed method was evaluated based on eight public datasets of real-world applications. It successfully recommended the best method for five datasets and the second best method for one dataset, which outperformed any of the four individual FS methods. Besides, the proposed method is computationally efficient for algorithm selection, leading to negligible additional time for the feature selection process. Thus, the paper contributes a novel method for effectively recommending which feature selection method to use for any new given dataset.

In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets. The proposed method mainly consists of three processes, including fuzzy sets generation using bootstrap, weighted combination of fuzzy sets and feature ranking based on defuzzification. We implemented the proposed method by combining four state-of-the-art feature selection methods and evaluated the performance based on three publicly available biomedical datasets using fivefold cross validation. Based on the feature selection results, our proposed method produced comparable (if not better) classification accuracies to the best of the individual feature selection methods for all evaluated datasets. More importantly, we also applied standard deviation and Pearson's correlation to measure the stability of the methods. Remarkably, our combination method achieved significantly higher stability than the four individual methods when variations and size reductions were introduced to the datasets.

© 2018 IEEE. In this paper, based on a fuzzy entropy feature selection framework, different methods have been implemented and compared to improve the key components of the framework. Those methods include the combinations of three ideal vector calculations, three maximal similarity classifiers and three fuzzy entropy functions. Different feature removal orders based on the fuzzy entropy values were also compared. The proposed method was evaluated on three publicly available biomedical datasets, including Wisconsin Breast Cancer(WBC), Wisconsin Diagnostic Breast Cancer(WDBC) and Parkinsons. From the experiments, we concluded the optimized combination of the ideal vector, similarity classifier and fuzzy entropy function for feature selection. The optimized framework was also compared with other six classical filter-based feature selection methods. The proposed method was ranked as one of the top performers together with the Correlation and ReliefF methods. The proposed method achieved classification accuracies of 96.97%, 94.85% and 78.23% for the WBC, WDBC and Parkinsons datasets respectively. More importantly, the proposed method achieved the most stable performance for all three datasets when the features being gradually removed. This indicates a better feature ranking performance than the other compared methods.

We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free breathing, high spatial, and temporal resolution abdominal magnetic resonance imaging sequences. Based on a radial golden-angle acquisition trajectory, our method enables a multidimensional self-gating signal to be extracted from the k -space data for more accurate motion representation. The k -space radial profiles are evenly divided into a number of overlapping groups based on their radial angles. MA is then used to simultaneously learn and align the low dimensional manifolds of all groups, and embed them into a common manifold. In the manifold, k -space profiles that represent similar respiratory positions are close to each other. Image reconstruction is performed by combining radial profiles with evenly distributed angles that are close in the manifold. Our method was evaluated on both 2-D and 3-D synthetic and in vivo data sets. On the synthetic data sets, our method achieved high correlation with the ground truth in terms of image intensity and virtual navigator values. Using the in vivo data, compared with a state-of-the-art approach based on the center of k -space gating, our method was able to make use of much richer profile data for self-gating, resulting in statistically significantly better quantitative measurements in terms of organ sharpness and image gradient entropy.

We present a novel framework for retrospective dynamic 3D volume reconstruction from a multi-slice MRI acquisition using manifold alignment. K-space data are continuously acquired under free breathing using a radial golden-angle trajectory in a slice-by-slice manner. Non-overlapping consecutive profiles that were acquired within a short time window are grouped together. All grouped profiles from all slices are then simultaneously embedded using manifold alignment into a common manifold space (MS), in which profiles that were acquired at similar respiratory states are close together. Subsequently, a 3D volume can be reconstructed at each of the grouped profile MS positions by combining profiles that are close in the MS. This enables the original multi-slice dataset to be used to reconstruct a dynamic 3D sequence based on the respiratory state correspondences established in the MS. Our method was evaluated on both synthetic and in vivo datasets. For the synthetic datasets, the reconstructed dynamic sequence achieved a normalised cross correlation of 0.98 and peak signal to noise ratio of 26.64 dB compared with the ground truth. For the in vivo datasets, based on sharpness measurements and visual comparison, our method performed better than reconstruction using an adapted central k-space gating method.

Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of 0.34 ±0.27 mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity.

We present a novel framework for estimating the 3D poses and shapes of the carpal bones from single view fluoroscopic sequences. A hybrid statistical model representing both the pose and shape variation of the carpal bones is built, based on a number of 3D CT data sets obtained from different subjects at different poses. Given a fluoroscopic sequence, the wrist pose, carpal bone pose and bone shapes are estimated iteratively by matching the statistical model with the 2D images. A specially designed cost function enables smoothed parameter estimation across frames and constrains local bone pose with a penalty term. We have evaluated the proposed method on both simulated data and real fluoroscopic sequences and demonstrated that the relative poses of carpal bones can be accurately estimated. One condition that may be assessed using this measurement is dissociation, where the distance between the bones is larger than normal. Scaphoid-Lunate dissociation is one of the most common of these. The error of the measured 3D Scaphoid-Lunate distances were 0.75±0.50 mm for simulated data (25 subjects) and 0.93±0.47 mm for real data (15 subjects). We also propose a method for constructing a "standard" pathology measurement tool for automatically detecting Scaphoid-Lunate dissociation conditions, based on single-view fluoroscopic sequences. For the simulated data, it produced 100% sensitivity and specificity. For the real data, it achieved 83% sensitivity and 78% specificity.

A novel framework is presented in this paper for simultaneous multi-label segmentation and registration of carpal bones which leads to efficient statistical model building. It combines the Grow Cut segmentation algorithm with rigid image registration for propagating the segmentation of bones to new poses or different individuals. The proposed framework compares favourably to the conventional segmentation and non- rigid registration methods, in terms of flexibility and computational time, for our CT data of carpal bones. The segmentation code was implemented in a GPU, running about 15 times faster than CPU code.