In this paper, we propose a dense multi-scale adaptive graph convolutional network (DMA-GCN) method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the following. First, our models integrate both local-level and global-level learning simultaneously. The local learning task aggregates spatial contextual information from aligned spatial neighborhoods of nodes, at multiple scales, while global learning explores pairwise affinities between nodes, located globally at different positions in the image. We propose two different structures of building models, whereby the local and global convolutional units are combined by following an alternating or a sequential manner. Secondly, based on the previous models, we develop the DMA-GCN network, by utilizing a densely connected architecture with residual skip connections. This is a deeper GCN structure, expanded over different block layers, thus being capable of providing more expressive node feature representations. Third, all units pertaining to the overall network are equipped with their individual adaptive graph learning mechanism, which allows the graph structures to be automatically learned during training. The proposed cartilage segmentation method is evaluated on the entire publicly available Osteoarthritis Initiative (OAI) cohort. To this end, we have devised a thorough experimental setup, with the goal of investigating the effect of several factors of our approach on the classification rates. Furthermore, we present exhaustive comparative results, considering traditional existing methods, six deep learning segmentation methods, and seven graph-based convolution methods, including the currently most representative models from this field. The obtained results demonstrate that the DMA-GCN outperforms all competing methods across all evaluation measures, providing $DSC=95.71\%$ and $DSC=94.02\%$ for the segmentation of femoral and tibial cartilage, respectively.

Leveraging the increased capacities of hypergraphs to model complex data structures, we propose in this article the Multi-view Hyper-Graph Convolutional Network (MVHGCN) to yield automated knee-joint cartilage segmentations from MRIs. The main properties of our approach are presented as follows: 1) Node features are obtained from multi-view (MV) acquisitions, corresponding to different feature extractors or image modalities. 2) Node embeddings are generated using a distributive MV convolution scheme which combines the various view-specific convolutions. These results are aggregated via an attention-based fusion module to automatically learn the weights of the different views. 3) Our model integrates both local and global level learning, simultaneously. Local hypergraph convolutions explore the relationships across the spatially aligned node libraries, while global hypergraph convolutions search for global affinities between nodes located at different positions within the image. 4) We propose two different blending schemes to combine local and global convolutions, namely, the cross-talk (CT) and the collaborative (COL) blending units, respectively. Using these units as building blocks, we construct the MVHGCN model, a deep network with enhanced feature representation and learning capabilities. The suggested segmentation method is evaluated on the publicly available Osteoarthritis Initiative (OAI) cohort. Specifically, we have designed a thorough experimental setup, including parameter sensitivity analysis and comparative results against a series of existing traditional methods, deep CNN models, and graph convolutional networks. The results show that MVHGCN outperforms the competing methods, achieving an overall cartilage segmentation score of $$\mathcal {DSC} = 95.81\%$$ and $$\mathcal {DSC} = 96.33\%$$ , for the CT and the COL blending, respectively.

Knee osteoarthritis (KOA) is a highly prevalent muscoloskeletal joint disorder affecting a significant portion of the population worldwide. Accurate predictions of KOA progression can assist clinicians in drawing preventive strategies for patients. In this paper, we present an integrated approach based on hypergraph convolutional networks (HGCNs) for longitudinal predictions of KOA grades and progressions from MRI images. We propose two novel models, namely, the C_Shape.Net and the predictor network. The C_Shape.Net operates on a hypergraph of volumetric nodes, especially designed to represent the surface and volumetric features of the cartilage. It encompasses deep HGCN convolutions, graph pooling, and readout operations in a hierarchy of layers, providing, at the output, expressive 3D shape descriptors of the cartilage volume. The predictor is a spatio-temporal HGCN network (ST_HGCN), following the sequence-to-sequence learning scheme. Concretely, it transforms sequences of knee representations at the historical stage into sequences of KOA predictions at the prediction stage. The predictor includes spatial HGCN convolutions, attention-based temporal fusion of feature embeddings at multiple layers, and a transformer module that generates longitudinal predictions at follow-up times. We present comprehensive experiments on the Osteoarthritis Initiative (OAI) cohort to evaluate the performance of our methodology for various tasks, including node classification, longitudinal KL grading, and progression. The basic finding of the experiments is that the larger the depth of the historical stage, the higher the accuracy of the obtained predictions in all tasks. For the maximum historic depth of four years, our method yielded an average balanced accuracy (BA) of 85.94% in KOA grading, and accuracies of 91.89% (+1), 88.11% (+2), 84.35% (+3), and 79.41% (+4) for the four consecutive follow-up visits. Under the same setting, we also achieved an average value of Area Under Curve (AUC) of 0.94 for the prediction of progression incidence, and follow-up AUC values of 0.81 (+1), 0.77 (+2), 0.73 (+3), and 0.68 (+4), respectively.

Knee osteoarthritis (KOA) is a most prevalent chronic muscoloskeletal disorder causing pain and functional impairment. Accurate predictions of KOA evolution are important for early interventions and preventive treatment planning. In this paper, we propose a novel dynamic hypergraph-based risk model (DyHRM) which integrates the encoder–decoder (ED) architecture with hypergraph convolutional neural networks (HGCNs). The risk model is used to generate longitudinal forecasts of KOA incidence and progression based on the knee evolution at a historical stage. DyHRM comprises two main parts, namely the dynamic hypergraph gated recurrent unit (DyHGRU) and the multi-view HGCN (MHGCN) networks. The ED-based DyHGRU follows the sequence-to-sequence learning approach. The encoder first transforms a knee sequence at the historical stage into a sequence of hidden states in a latent space. The Attention-based Context Transformer (ACT) is designed to identify important temporal trends in the encoder’s state sequence, while the decoder is used to generate sequences of KOA progression, at the prediction stage. MHGCN conducts multi-view spatial HGCN convolutions of the original knee data at each step of the historic stage. The aim is to acquire more comprehensive feature representations of nodes by exploiting different hyperedges (views), including the global shape descriptors of the cartilage volume, the injury history, and the demographic risk factors. In addition to DyHRM, we also propose the HyGraphSMOTE method to confront the inherent class imbalance problem in KOA datasets, between the knee progressors (minority) and non-progressors (majority). Embedded in MHGCN, the HyGraphSMOTE algorithm tackles data balancing in a systematic way, by generating new synthetic node sequences of the minority class via interpolation. Extensive experiments are conducted using the Osteoarthritis Initiative (OAI) cohort to validate the accuracy of longitudinal predictions acquired by DyHRM under different definition criteria of KOA incidence and progression. The basic finding of the experiments is that the larger the historic depth, the higher the accuracy of the obtained forecasts ahead. Comparative results demonstrate the efficacy of DyHRM against other state-of-the-art methods in this field.

The forthcoming Copernicus Sentinel Expansion missions CHIME and LSTM offer new opportunities for soil monitoring through the complementary use of hyperspectral reflectance and thermal emissivity. This study evaluated the predictive value of visible to shortwave infrared (VSWIR; 350–2500 nm) reflectance and longwave infrared (LWIR) emissivity, used separately and in combination, for estimating soil organic carbon (SOC), clay, silt, and sand. High-resolution laboratory VSWIR reflectance and LWIR emissivity spectra from 787 soil samples across five Mediterranean pilot sites were resampled using preliminary CHIME and LSTM instrument spectral response functions. Random forest regression was evaluated using VSWIR-only and LWIR-only baselines together with three spectral-fusion strategies: early fusion, unweighted late fusion, and stacked fusion. The robustness of fusion gains was further assessed using 100 repeated site- and target-stratified 80/20 holdouts.VSWIR provided the dominant predictive information for all four properties, whereas LWIR-only models were consistently weaker. However, adding LWIR information improved prediction when integrated through suitable fusion strategies. On the fixed Kennard–Stone test set, the strongest fused models reached R^2 values of 0.80 for SOC, 0.85 for clay, 0.86 for silt, and 0.87 for sand, with the largest improvements relative to the VSWIR-only baseline observed for silt and sand. Repeated-holdout analysis confirmed that early-fusion gains were highly consistent across alternative data partitions, while stacked fusion provided additional benefits for selected properties. These results demonstrate complementary predictive information in the VSWIR and LWIR domains under controlled laboratory and mission-resampled conditions and provide a basis for future evaluation of joint CHIME–LSTM soil property retrieval.