Computerized Medical Imaging and Graphics最新文献

{"title":"Adaptive fusion of dual-view for grading prostate cancer.","authors":"Yaolin He, Bowen Li, Ruimin He, Guangming Fu, Dan Sun, Dongyong Shan, Zijian Zhang","doi":"10.1016/j.compmedimag.2024.102479","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2024.102479","url":null,"abstract":"<p><p>Accurate preoperative grading of prostate cancer is crucial for assisted diagnosis. Multi-parametric magnetic resonance imaging (MRI) is a commonly used non-invasive approach, however, the interpretation of MRI images is still subject to significant subjectivity due to variations in physicians' expertise and experience. To achieve accurate, non-invasive, and efficient grading of prostate cancer, this paper proposes a deep learning method that adaptively fuses dual-view MRI images. Specifically, a dual-view adaptive fusion model is designed. The model employs encoders to extract embedded features from two MRI sequences: T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC). The model reconstructs the original input images using the embedded features and adopts a cross-embedding fusion module to adaptively fuse the embedded features from the two views. Adaptive fusion refers to dynamically adjusting the fusion weights of the features from the two views according to different input samples, thereby fully utilizing complementary information. Furthermore, the model adaptively weights the prediction results from the two views based on uncertainty estimation, further enhancing the grading performance. To verify the importance of effective multi-view fusion for prostate cancer grading, extensive experiments are designed. The experiments evaluate the performance of single-view models, dual-view models, and state-of-the-art multi-view fusion algorithms. The results demonstrate that the proposed dual-view adaptive fusion method achieves the best grading performance, confirming its effectiveness for assisted grading diagnosis of prostate cancer. This study provides a novel deep learning solution for preoperative grading of prostate cancer, which has the potential to assist clinical physicians in making more accurate diagnostic decisions and has significant clinical application value.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"119 ","pages":"102479"},"PeriodicalIF":5.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Portable head CT motion artifact correction via diffusion-based generative model.","authors":"Zhennong Chen, Siyeop Yoon, Quirin Strotzer, Rehab Naeem Khalid, Matthew Tivnan, Quanzheng Li, Rajiv Gupta, Dufan Wu","doi":"10.1016/j.compmedimag.2024.102478","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2024.102478","url":null,"abstract":"<p><p>Portable head CT images often suffer motion artifacts due to the prolonged scanning time and critically ill patients who are unable to hold still. Image-domain motion correction is attractive for this application as it does not require CT projection data. This paper describes and evaluates a generative model based on conditional diffusion to correct motion artifacts in portable head CT scans. This model was trained to find the motion-free CT image conditioned on the paired motion-corrupted image. Our method utilizes histogram equalization to resolve the intensity range discrepancy of skull and brain tissue and an advanced Elucidated Diffusion Model (EDM) framework for faster sampling and better motion correction performance. Our EDM framework is superior in correcting artifacts in the brain tissue region and across the entire image compared to CNN-based methods and standard diffusion approach (DDPM) in a simulation study and a phantom study with known motion-free ground truth. Furthermore, we conducted a reader study on real-world portable CT scans to demonstrate improvement of image quality using our method.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"119 ","pages":"102478"},"PeriodicalIF":5.4,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-hoc out-of-distribution detection for cardiac MRI segmentation.","authors":"Tewodros Weldebirhan Arega, Stéphanie Bricq, Fabrice Meriaudeau","doi":"10.1016/j.compmedimag.2024.102476","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2024.102476","url":null,"abstract":"<p><p>In real-world scenarios, medical image segmentation models encounter input images that may deviate from the training images in various ways. These differences can arise from changes in image scanners and acquisition protocols, or even the images can come from a different modality or domain. When the model encounters these out-of-distribution (OOD) images, it can behave unpredictably. Therefore, it is important to develop a system that handles such out-of-distribution images to ensure the safe usage of the models in clinical practice. In this paper, we propose a post-hoc out-of-distribution (OOD) detection method that can be used with any pre-trained segmentation model. Our method utilizes multi-scale representations extracted from the encoder blocks of the segmentation model and employs Mahalanobis distance as a metric to measure the similarity between the input image and the in-distribution images. The segmentation model is pre-trained on a publicly available cardiac short-axis cine MRI dataset. The detection performance of the proposed method is evaluated on 13 different OOD datasets, which can be categorized as near, mild, and far OOD datasets based on their similarity to the in-distribution dataset. The results show that our method outperforms state-of-the-art feature space-based and uncertainty-based OOD detection methods across the various OOD datasets. Our method successfully detects near, mild, and far OOD images with high detection accuracy, showcasing the advantage of using the multi-scale and semantically rich representations of the encoder. In addition to the feature-based approach, we also propose a Dice coefficient-based OOD detection method, which demonstrates superior performance for adversarial OOD detection and shows a high correlation with segmentation quality. For the uncertainty-based method, despite having a strong correlation with the quality of the segmentation results in the near OOD datasets, they failed to detect mild and far OOD images, indicating the weakness of these methods when the images are more dissimilar. Future work will explore combining Mahalanobis distance and uncertainty scores for improved detection of challenging OOD images that are difficult to segment.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"119 ","pages":"102476"},"PeriodicalIF":5.4,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guidelines for cerebrovascular segmentation: Managing imperfect annotations in the context of semi-supervised learning.","authors":"Pierre Rougé, Pierre-Henri Conze, Nicolas Passat, Odyssée Merveille","doi":"10.1016/j.compmedimag.2024.102474","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2024.102474","url":null,"abstract":"<p><p>Segmentation in medical imaging is an essential and often preliminary task in the image processing chain, driving numerous efforts towards the design of robust segmentation algorithms. Supervised learning methods achieve excellent performances when fed with a sufficient amount of labeled data. However, such labels are typically highly time-consuming, error-prone and expensive to produce. Alternatively, semi-supervised learning approaches leverage both labeled and unlabeled data, and are very useful when only a small fraction of the dataset is labeled. They are particularly useful for cerebrovascular segmentation, given that labeling a single volume requires several hours for an expert. In addition to the challenge posed by insufficient annotations, there are concerns regarding annotation consistency. The task of annotating the cerebrovascular tree is inherently ambiguous. Due to the discrete nature of images, the borders and extremities of vessels are often unclear. Consequently, annotations heavily rely on the expert subjectivity and on the underlying clinical objective. These discrepancies significantly increase the complexity of the segmentation task for the model and consequently impair the results. Consequently, it becomes imperative to provide clinicians with precise guidelines to improve the annotation process and construct more uniform datasets. In this article, we investigate the data dependency of deep learning methods within the context of imperfect data and semi-supervised learning, for cerebrovascular segmentation. Specifically, this study compares various state-of-the-art semi-supervised methods based on unsupervised regularization and evaluates their performance in diverse quantity and quality data scenarios. Based on these experiments, we provide guidelines for the annotation and training of cerebrovascular segmentation models.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"119 ","pages":"102474"},"PeriodicalIF":5.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-view contrastive learning and semi-supervised self-distillation framework for early recurrence prediction in ovarian cancer.","authors":"Chi Dong, Yujiao Wu, Bo Sun, Jiayi Bo, Yufei Huang, Yikang Geng, Qianhui Zhang, Ruixiang Liu, Wei Guo, Xingling Wang, Xiran Jiang","doi":"10.1016/j.compmedimag.2024.102477","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2024.102477","url":null,"abstract":"<p><strong>Objective: </strong>This study presents a novel framework that integrates contrastive learning and knowledge distillation to improve early ovarian cancer (OC) recurrence prediction, addressing the challenges posed by limited labeled data and tumor heterogeneity.</p><p><strong>Methods: </strong>The research utilized CT imaging data from 585 OC patients, including 142 cases with complete follow-up information and 125 cases with unknown recurrence status. To pre-train the teacher network, 318 unlabeled images were sourced from public datasets (TCGA-OV and PLAGH-202-OC). Multi-view contrastive learning (MVCL) was employed to generate multi-view 2D tumor slices, enhancing the teacher network's ability to extract features from complex, heterogeneous tumors with high intra-class variability. Building on this foundation, the proposed semi-supervised multi-task self-distillation (Semi-MTSD) framework integrated OC subtyping as an auxiliary task using multi-task learning (MTL). This approach allowed the co-training of a student network for recurrence prediction, leveraging both labeled and unlabeled data to improve predictive performance in data-limited settings. The student network's performance was assessed using preoperative CT images with known recurrence outcomes. Evaluation metrics included area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), F1 score, floating-point operations (FLOPs), parameter count, training time, inference time, and mean corruption error (mCE).</p><p><strong>Results: </strong>The proposed framework achieved an ACC of 0.862, an AUC of 0.916, a SPE of 0.895, and an F1 score of 0.831, surpassing existing methods for OC recurrence prediction. Comparative and ablation studies validated the model's robustness, particularly in scenarios characterized by data scarcity and tumor heterogeneity.</p><p><strong>Conclusion: </strong>The MVCL and Semi-MTSD framework demonstrates significant advancements in OC recurrence prediction, showcasing strong generalization capabilities in complex, data-constrained environments. This approach offers a promising pathway toward more personalized treatment strategies for OC patients.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"119 ","pages":"102477"},"PeriodicalIF":5.4,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of the Segment Anything Model (SAM) for medical image analysis: Accomplishments and perspectives.","authors":"Mudassar Ali, Tong Wu, Haoji Hu, Qiong Luo, Dong Xu, Weizeng Zheng, Neng Jin, Chen Yang, Jincao Yao","doi":"10.1016/j.compmedimag.2024.102473","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2024.102473","url":null,"abstract":"<p><p>The purpose of this paper is to provide an overview of the developments that have occurred in the Segment Anything Model (SAM) within the medical image segmentation category over the course of the past year. However, SAM has demonstrated notable achievements in adapting to medical image segmentation tasks through fine-tuning on medical datasets, transitioning from 2D to 3D datasets, and optimizing prompting engineering. This is despite the fact that direct application on medical datasets has shown mixed results. Despite the difficulties, the paper emphasizes the significant potential that SAM possesses in the field of medical segmentation. One of the suggested directions for the future is to investigate the construction of large-scale datasets, to address multi-modal and multi-scale information, to integrate with semi-supervised learning structures, and to extend the application methods of SAM in clinical settings. In addition to making a significant contribution to the field of medical segmentation.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"119 ","pages":"102473"},"PeriodicalIF":5.4,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer-assisted diagnosis for axillary lymph node metastasis of early breast cancer based on transformer with dual-modal adaptive mid-term fusion using ultrasound elastography","authors":"Chihao Gong ,&nbsp;Yinglan Wu ,&nbsp;Guangyuan Zhang ,&nbsp;Xuan Liu ,&nbsp;Xiaoyao Zhu ,&nbsp;Nian Cai ,&nbsp;Jian Li","doi":"10.1016/j.compmedimag.2024.102472","DOIUrl":"10.1016/j.compmedimag.2024.102472","url":null,"abstract":"<div><div>Accurate preoperative qualitative assessment of axillary lymph node metastasis (ALNM) in early breast cancer patients is crucial for precise clinical staging and selection of axillary treatment strategies. Although previous studies have introduced artificial intelligence (AI) to enhance the assessment performance of ALNM, they all focus on the prediction performances of their AI models and neglect the clinical assistance to the radiologists, which brings some issues to the clinical practice. To this end, we propose a human–AI collaboration strategy for ALNM diagnosis of early breast cancer, in which a novel deep learning framework, termed DAMF-former, is designed to assist radiologists in evaluating ALNM. Specifically, the DAMF-former focuses on the axillary region rather than the primary tumor area in previous studies. To mimic the radiologists’ alternative integration of the UE images of the target axillary lymph nodes for comprehensive analysis, adaptive mid-term fusion is proposed to alternatively extract and adaptively fuse the high-level features from the dual-modal UE images (i.e., B-mode ultrasound and Shear Wave Elastography). To further improve the diagnostic outcome of the DAMF-former, an adaptive Youden index scheme is proposed to deal with the fully fused dual-modal UE image features at the end of the framework, which can balance the diagnostic performance in terms of sensitivity and specificity. The clinical experiment indicates that the designed DAMF-former can assist and improve the diagnostic abilities of less-experienced radiologists for ALNM. Especially, the junior radiologists can significantly improve the diagnostic outcome from 0.807 AUC [95% CI: 0.781, 0.830] to 0.883 AUC [95% CI: 0.861, 0.902] (<span><math><mi>P</mi></math></span>-value <span><math><mo>&lt;</mo></math></span>0.0001). Moreover, there are great agreements among radiologists of different levels when assisted by the DAMF-former (Kappa value ranging from 0.805 to 0.895; <span><math><mi>P</mi></math></span>-value <span><math><mo>&lt;</mo></math></span>0.0001), suggesting that less-experienced radiologists can potentially achieve a diagnostic level similar to that of experienced radiologists through human–AI collaboration. This study explores a potential solution to human–AI collaboration for ALNM diagnosis based on UE images.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"119 ","pages":"Article 102472"},"PeriodicalIF":5.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty-aware regression model to predict post-operative visual acuity in patients with macular holes","authors":"Burak Kucukgoz ,&nbsp;Ke Zou ,&nbsp;Declan C. Murphy ,&nbsp;David H. Steel ,&nbsp;Boguslaw Obara ,&nbsp;Huazhu Fu","doi":"10.1016/j.compmedimag.2024.102461","DOIUrl":"10.1016/j.compmedimag.2024.102461","url":null,"abstract":"<div><div>Full-thickness macular holes are a relatively common and visually disabling condition with a prevalence of approximately 0.5% in the over-40-year-old age group. If left untreated, the hole typically enlarges, reducing visual acuity (VA) below the definition of blindness in the eye affected. They are now routinely treated with surgery, which can close the hole and improve vision in most cases. The extent of improvement, however, is variable and dependent on the size of the hole and other features which can be discerned in spectral-domain optical coherence tomography imaging, which is now routinely available in eye clinics globally. Artificial intelligence (AI) models have been developed to enable surgical decision-making and have achieved relatively high predictive performance. However, their black-box behavior is opaque to users and uncertainty associated with their predictions is not typically stated, leading to a lack of trust among clinicians and patients. In this paper, we describe an uncertainty-aware regression model (U-ARM) for predicting VA for people undergoing macular hole surgery using preoperative spectral-domain optical coherence tomography images, achieving an MAE of 6.07, RMSE of 9.11 and R2 of 0.47 in internal tests, and an MAE of 6.49, RMSE of 9.49, and R2 of 0.42 in external tests. In addition to predicting VA following surgery, U-ARM displays its associated uncertainty, a <span><math><mi>p</mi></math></span>-value of &lt;0.005 in internal and external tests, showing the predictions are not due to random chance. We then qualitatively evaluated the performance of U-ARM. Lastly, we demonstrate out-of-sample data performance, generalizing well to data outside the training distribution, low-quality images, and unseen instances not encountered during training. The results show that U-ARM outperforms commonly used methods in terms of prediction and reliability. U-ARM is thus a promising approach for clinical settings and can improve the reliability of AI models in predicting VA.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"119 ","pages":"Article 102461"},"PeriodicalIF":5.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised learning on dual-sequence magnetic resonance imaging for automatic segmentation of nasopharyngeal carcinoma","authors":"Zongyou Cai ,&nbsp;Zhangnan Zhong ,&nbsp;Haiwei Lin ,&nbsp;Bingsheng Huang ,&nbsp;Ziyue Xu ,&nbsp;Bin Huang ,&nbsp;Wei Deng ,&nbsp;Qiting Wu ,&nbsp;Kaixin Lei ,&nbsp;Jiegeng Lyu ,&nbsp;Yufeng Ye ,&nbsp;Hanwei Chen ,&nbsp;Jian Zhang","doi":"10.1016/j.compmedimag.2024.102471","DOIUrl":"10.1016/j.compmedimag.2024.102471","url":null,"abstract":"<div><div>Automating the segmentation of nasopharyngeal carcinoma (NPC) is crucial for therapeutic procedures but presents challenges given the hurdles in amassing extensively annotated datasets. Although previous studies have applied self-supervised learning to capitalize on unlabeled data to improve segmentation performance, these methods often overlooked the benefits of dual-sequence magnetic resonance imaging (MRI). In the present study, we incorporated self-supervised learning with a saliency transformation module using unlabeled dual-sequence MRI for accurate NPC segmentation. 44 labeled and 72 unlabeled patients were collected to develop and evaluate our network. Impressively, our network achieved a mean Dice similarity coefficient (DSC) of 0.77, which is consistent with a previous study that relied on a training set of 4,100 annotated cases. The results further revealed that our approach required minimal adjustments, primarily <span><math><mo>&lt;</mo></math></span> 20% tweak in the DSC, to meet clinical standards. By enhancing the automatic segmentation of NPC, our method alleviates the annotation burden on oncologists, curbs subjectivity, and ensures reliable NPC delineation.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102471"},"PeriodicalIF":5.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single color digital H&E staining with In-and-Out Net","authors":"Mengkun Chen ,&nbsp;Yen-Tung Liu ,&nbsp;Fadeel Sher Khan ,&nbsp;Matthew C. Fox ,&nbsp;Jason S. Reichenberg ,&nbsp;Fabiana C.P.S. Lopes ,&nbsp;Katherine R. Sebastian ,&nbsp;Mia K. Markey ,&nbsp;James W. Tunnell","doi":"10.1016/j.compmedimag.2024.102468","DOIUrl":"10.1016/j.compmedimag.2024.102468","url":null,"abstract":"<div><div>Digital staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, digital staining offers an efficient and low-infrastructure alternative. Researchers can expedite tissue analysis without physical sectioning by leveraging microscopy-based techniques, such as confocal microscopy. However, interpreting grayscale or pseudo-color microscopic images remains challenging for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, designed explicitly for digital staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&amp;E) stained images. Using aluminum chloride preprocessing for skin tissue, we enhance nuclei contrast in RCM images. We trained the model with digital H&amp;E labels featuring two fluorescence channels, eliminating the need for image registration and providing pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for digital staining tasks and advancing the field of histological image analysis.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"118 ","pages":"Article 102468"},"PeriodicalIF":5.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}