Computerized Medical Imaging and Graphics最新文献

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Accurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relies on the quality and quantity of the training data. Most of such works employed small-scale public datasets, without proving the efficacy of generalization to external datasets. This study explored the optimization of pancreas segmentation accuracy by pinpointing the ideal dataset size, understanding resource implications, examining manual refinement impact, and assessing the influence of anatomical subregions. We present the AIMS-1300 dataset encompassing 1,300 CT scans. Its manual annotation by medical experts required 938 h. A 2.5D UNet was implemented to assess the impact of training sample size on segmentation accuracy by partitioning the original AIMS-1300 dataset into 11 smaller subsets of progressively increasing numerosity. The findings revealed that training sets exceeding 440 CTs did not lead to better segmentation performance. In contrast, nnU-Net and UNet with Attention Gate reached a plateau for 585 CTs. Tests on generalization on the publicly available AMOS-CT dataset confirmed this outcome. As the size of the partition of the AIMS-1300 training set increases, the number of error slices decreases, reaching a minimum with 730 and 440 CTs, for AIMS-1300 and AMOS-CT datasets, respectively. Segmentation metrics on the AIMS-1300 and AMOS-CT datasets improved more on the head than the body and tail of the pancreas as the dataset size increased. By carefully considering the task and the characteristics of the available data, researchers can develop deep learning models without sacrificing performance even with limited data. This could accelerate developing and deploying artificial intelligence tools for pancreas surgery and other surgical data science applications.

Oral squamous cell carcinoma recognition presents a challenge due to late diagnosis and costly data acquisition. A cost-efficient, computerized screening system is crucial for early disease detection, minimizing the need for expert intervention and expensive analysis. Besides, transparency is essential to align these systems with critical sector applications. Explainable Artificial Intelligence (XAI) provides techniques for understanding models. However, current XAI is mostly data-driven and focused on addressing developers’ requirements of improving models rather than clinical users’ demands for expressing relevant insights. Among different XAI strategies, we propose a solution composed of Case-Based Reasoning paradigm to provide visual output explanations and Informed Deep Learning (IDL) to integrate medical knowledge within the system. A key aspect of our solution lies in its capability to handle data imperfections, including labeling inaccuracies and artifacts, thanks to an ensemble architecture on top of the deep learning (DL) workflow. We conducted several experimental benchmarks on a dataset collected in collaboration with medical centers. Our findings reveal that employing the IDL approach yields an accuracy of 85%, surpassing the 77% accuracy achieved by DL alone. Furthermore, we measured the human-centered explainability of the two approaches and IDL generates explanations more congruent with the clinical user demands.

Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma expansion (HE) is an increase in the bleeding that affects 30%–38% of hemorrhagic stroke patients. It is observed within 24 h of onset and associated with patient worsening. Clinically it is relevant to detect the patients that will develop HE from their initial computed tomography (CT) scans which could improve patient management and treatment decisions. However, this is a significant challenge due to the predictive nature of the task and its low prevalence, which hinders the availability of large datasets with the required longitudinal information. In this work, we present an end-to-end deep learning framework capable of predicting which cases will exhibit HE using only the initial basal image. We introduce a deep learning framework based on the 2D EfficientNet B0 model to predict the occurrence of HE using initial non-contrasted CT scans and their corresponding lesion annotation as priors. We used an in-house acquired dataset of 122 ICH patients, including 35 HE cases, containing longitudinal CT scans with manual lesion annotations in both basal and follow-up (obtained within 24 h after the basal scan). Experiments were conducted using a 5-fold cross-validation strategy. We addressed the limited data problem by incorporating synthetic images into the training process. To the best of our knowledge, our approach is novel in the field of HE prediction, being the first to use image synthesis to enhance results. We studied different scenarios such as training only with the original scans, using standard image augmentation techniques, and using synthetic image generation. The best performance was achieved by adding five generated versions of each image, along with standard data augmentation, during the training process. This significantly improved ($p=0.0003$) the performance obtained with our baseline model using directly the original CT scans from an Accuracy of 0.56 to 0.84, F1-Score of 0.53 to 0.82, Sensitivity of 0.51 to 0.77, and Specificity of 0.60 to 0.91, respectively. The proposed approach shows promising results in predicting HE, especially with the inclusion of synthetically generated images. The obtained results highlight the significance of this research direction, which has the potential to improve the clinical management of patients with hemorrhagic stroke. The code is available at: https://github.com/NIC-VICOROB/HE-prediction-SynthCT.

CycleGAN has been leveraged to synthesize a CT image from an available MR image after trained on unpaired data. Due to the lack of direct constraints between the synthetic and the input images, CycleGAN cannot guarantee structural consistency and often generates inaccurate mappings that shift the anatomy, which is highly undesirable for downstream clinical applications such as MRI-guided radiotherapy treatment planning and PET/MRI attenuation correction. In this paper, we propose a cycle-consistent and semantics-preserving generative adversarial network, referred as CycleSGAN, for unpaired MR-to-CT image synthesis. Our design features a novel and generic way to incorporate semantic information into CycleGAN. This is done by designing a pair of three-player games within the CycleGAN framework where each three-player game consists of one generator and two discriminators to formulate two distinct types of adversarial learning: appearance adversarial learning and structure adversarial learning. These two types of adversarial learning are alternately trained to ensure both realistic image synthesis and semantic structure preservation. Results on unpaired hip MR-to-CT image synthesis show that our method produces better synthetic CT images in both accuracy and visual quality as compared to other state-of-the-art (SOTA) unpaired MR-to-CT image synthesis methods.

Lung cancer has the highest mortality rate among cancers. The commonly used clinical method for diagnosing lung cancer is the CT-guided percutaneous transthoracic lung biopsy (CT-PTLB), but this method requires a high level of clinical experience from doctors. In this work, an automatic path planning method for CT-PTLB is proposed to provide doctors with auxiliary advice on puncture paths. The proposed method comprises three steps: preprocessing, initial path selection, and path evaluation. During preprocessing, the chest organs required for subsequent path planning are segmented. During the initial path selection, a target point selection method for selecting biopsy samples according to biopsy sampling requirements is proposed, which includes a down-sampling algorithm suitable for different nodule shapes. Entry points are selected according to the selected target points and clinical constraints. During the path evaluation, the clinical needs of lung biopsy surgery are first quantified as path evaluation indicators and then divided according to their evaluation perspective into risk and execution indicators. Then, considering the impact of the correlation between indicators, a path scoring system based on the double spherical constraint Pareto and the importance-correlation degree of the indicators is proposed to evaluate the comprehensive performance of the planned paths. The proposed method is retrospectively tested on 6 CT images and prospectively tested on 25 CT images. The experimental results indicate that the method proposed in this work can be used to plan feasible puncture paths for different cases and can serve as an auxiliary tool for lung biopsy surgery.