Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention最新文献_第3页

An approach to building foundation models for brain image analysis. 建立脑图像分析基础模型的方法。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-23 DOI: 10.1007/978-3-031-72390-2_40

Davood Karimi

{"title":"An approach to building foundation models for brain image analysis.","authors":"Davood Karimi","doi":"10.1007/978-3-031-72390-2_40","DOIUrl":"https://doi.org/10.1007/978-3-031-72390-2_40","url":null,"abstract":"Existing machine learning methods for brain image analysis are mostly based on supervised training. They require large labeled datasets, which can be costly or impossible to obtain. Moreover, the trained models are useful only for the narrow task defined by the labels. In this work, we developed a new method, based on the concept of foundation models, to overcome these limitations. Our model is an attention-based neural network that is trained using a novel self-supervised approach. Specifically, the model is trained to generate brain images in a patch-wise manner, thereby learning the brain structure. To facilitate learning of image details, we propose a new method that encodes high-frequency information using convolutional kernels with random weights. We trained our model on a pool of 10 public datasets. We then applied the model on five independent datasets to perform segmentation, lesion detection, denoising, and brain age estimation. Results showed that the foundation model achieved competitive or better results on all tasks, while significantly reducing the required amount of labeled training data. Our method enables leveraging large unlabeled neuroimaging datasets to effectively address diverse brain image analysis tasks and reduce the time and cost requirements of acquiring labels.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15012 ","pages":"421-431"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12033034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Whole Slide Image Classification with Discriminative and Contrastive Learning. 用判别和对比学习增强整个幻灯片图像分类。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-14 DOI: 10.1007/978-3-031-72083-3_10

Peixian Liang, Hao Zheng, Hongming Li, Yuxin Gong, Spyridon Bakas, Yong Fan

{"title":"Enhancing Whole Slide Image Classification with Discriminative and Contrastive Learning.","authors":"Peixian Liang, Hao Zheng, Hongming Li, Yuxin Gong, Spyridon Bakas, Yong Fan","doi":"10.1007/978-3-031-72083-3_10","DOIUrl":"10.1007/978-3-031-72083-3_10","url":null,"abstract":"Whole slide image (WSI) classification plays a crucial role in digital pathology data analysis. However, the immense size of WSIs and the absence of fine-grained sub-region labels pose significant challenges for accurate WSI classification. Typical classification-driven deep learning methods often struggle to generate informative image representations, which can compromise the robustness of WSI classification. In this study, we address this challenge by incorporating both discriminative and contrastive learning techniques for WSI classification. Different from the existing contrastive learning methods for WSI classification that primarily rely on pseudo labels assigned to patches based on the WSI-level labels, our approach takes a different route to directly focus on constructing positive and negative samples at the WSI-level. Specifically, we select a subset of representative image patches to represent WSIs and create positive and negative samples at the WSI-level, facilitating effective learning of informative image features. Experimental results on two datasets and ablation studies have demonstrated that our method significantly improved the WSI classification performance compared to state-of-the-art deep learning methods and enabled learning of informative features that promoted robustness of the WSI classification.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15004 ","pages":"102-112"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11877581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143568358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection. 用于不确定性感知前列腺癌检测的跨片注意力和证据临界损失。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-06 DOI: 10.1007/978-3-031-72111-3_11

Alex Ling Yu Hung, Haoxin Zheng, Kai Zhao, Kaifeng Pang, Demetri Terzopoulos, Kyunghyun Sung

{"title":"Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection.","authors":"Alex Ling Yu Hung, Haoxin Zheng, Kai Zhao, Kaifeng Pang, Demetri Terzopoulos, Kyunghyun Sung","doi":"10.1007/978-3-031-72111-3_11","DOIUrl":"10.1007/978-3-031-72111-3_11","url":null,"abstract":"Current deep learning-based models typically analyze medical images in either 2D or 3D albeit disregarding volumetric information or suffering sub-optimal performance due to the anisotropic resolution of MR data. Furthermore, providing an accurate uncertainty estimation is beneficial to clinicians, as it indicates how confident a model is about its prediction. We propose a novel 2.5D cross-slice attention model that utilizes both global and local information, along with an evidential critical loss, to perform evidential deep learning for the detection in MR images of prostate cancer, one of the most common cancers and a leading cause of cancer-related death in men. We perform extensive experiments with our model on two different datasets and achieve state-of-the-art performance in prostate cancer detection along with improved epistemic uncertainty estimation. The implementation of the model is available at https://github.com/aL3x-O-o-Hung/GLCSA_ECLoss.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15008 ","pages":"113-123"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11646698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conditional Diffusion Model with Spatial Attention and Latent Embedding for Medical Image Segmentation. 基于空间注意和隐嵌入的条件扩散模型医学图像分割。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2024-10-01 Epub Date: 2024-10-03 DOI: 10.1007/978-3-031-72114-4_20

Behzad Hejrati, Soumyanil Banerjee, Carri Glide-Hurst, Ming Dong

{"title":"Conditional Diffusion Model with Spatial Attention and Latent Embedding for Medical Image Segmentation.","authors":"Behzad Hejrati, Soumyanil Banerjee, Carri Glide-Hurst, Ming Dong","doi":"10.1007/978-3-031-72114-4_20","DOIUrl":"10.1007/978-3-031-72114-4_20","url":null,"abstract":"Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding (cDAL) for medical image segmentation. In cDAL, a convolutional neural network (CNN) based discriminator is used at every time-step of the diffusion process to distinguish between the generated labels and the real ones. A spatial attention map is computed based on the features learned by the discriminator to help cDAL generate more accurate segmentation of discriminative regions in an input image. Additionally, we incorporated a random latent embedding into each layer of our model to significantly reduce the number of training and sampling time-steps, thereby making it much faster than other diffusion models for image segmentation. We applied cDAL on 3 publicly available medical image segmentation datasets (MoNuSeg, Chest X-ray and Hippocampus) and observed significant qualitative and quantitative improvements with higher Dice scores and mIoU over the state-of-the-art algorithms. The source code is publicly available at https://github.com/Hejrati/cDAL/.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15009 ","pages":"202-212"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities. MRIS：多模态图像合成的多模态检索方法。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43999-5_26

Boqi Chen, Marc Niethammer

{"title":"MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities.","authors":"Boqi Chen, Marc Niethammer","doi":"10.1007/978-3-031-43999-5_26","DOIUrl":"10.1007/978-3-031-43999-5_26","url":null,"abstract":"Multiple imaging modalities are often used for disease diagnosis, prediction, or population-based analyses. However, not all modalities might be available due to cost, different study designs, or changes in imaging technology. If the differences between the types of imaging are small, data harmonization approaches can be used; for larger changes, direct image synthesis approaches have been explored. In this paper, we develop an approach based on multi-modal metric learning to synthesize images of diverse modalities. We use metric learning via multi-modal image retrieval, resulting in embeddings that can relate images of different modalities. Given a large image database, the learned image embeddings allow us to use k-nearest neighbor (k-NN) regression for image synthesis. Our driving medical problem is knee osteoarthritis (KOA), but our developed method is general after proper image alignment. We test our approach by synthesizing cartilage thickness maps obtained from 3D magnetic resonance (MR) images using 2D radiographs. Our experiments show that the proposed method outperforms direct image synthesis and that the synthesized thickness maps retain information relevant to downstream tasks such as progression prediction and Kellgren-Lawrence grading (KLG). Our results suggest that retrieval approaches can be used to obtain high-quality and meaningful image synthesis results given large image databases.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14229 ","pages":"271-281"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11378323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142157088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weakly Supervised Cerebellar Cortical Surface Parcellation with Self-Visual Representation Learning. 自我视觉表征学习的弱监督小脑皮质表面包裹化。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43993-3_42

Zhengwang Wu, Jiale Cheng, Fenqiang Zhao, Ya Wang, Yue Sun, Dajiang Zhu, Tianming Liu, Valerie Jewells, Weili Lin, Li Wang, Gang Li

{"title":"Weakly Supervised Cerebellar Cortical Surface Parcellation with Self-Visual Representation Learning.","authors":"Zhengwang Wu, Jiale Cheng, Fenqiang Zhao, Ya Wang, Yue Sun, Dajiang Zhu, Tianming Liu, Valerie Jewells, Weili Lin, Li Wang, Gang Li","doi":"10.1007/978-3-031-43993-3_42","DOIUrl":"https://doi.org/10.1007/978-3-031-43993-3_42","url":null,"abstract":"The cerebellum (i.e., little brain) plays an important role in motion and balances control abilities, despite its much smaller size and deeper sulci compared to the cerebrum. Previous cerebellum studies mainly relied on and focused on conventional volumetric analysis, which ignores the extremely deep and highly convoluted nature of the cerebellar cortex. To better reveal localized functional and structural changes, we propose cortical surface-based analysis of the cerebellar cortex. Specifically, we first reconstruct the cerebellar cortical surfaces to represent and characterize the highly folded cerebellar cortex in a geometrically accurate and topologically correct manner. Then, we propose a novel method to automatically parcellate the cerebellar cortical surface into anatomically meaningful regions by a weakly supervised graph convolutional neural network. Instead of relying on registration or requiring mapping the cerebellar surface to a sphere, which are either inaccurate or have large geometric distortions due to the deep cerebellar sulci, our learning-based model directly deals with the original cerebellar cortical surface by decomposing this challenging task into two steps. First, we learn the effective representation of the cerebellar cortical surface patches with a contrastive self-learning framework. Then, we map the learned representations to parcellation labels. We have validated our method using data from the Baby Connectome Project and the experimental results demonstrate its superior effectiveness and accuracy, compared to existing methods.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14227 ","pages":"429-438"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12030008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers? 修剪如何影响长尾多标签医学图像分类器？

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43904-9_64

Gregory Holste, Ziyu Jiang, Ajay Jaiswal, Maria Hanna, Shlomo Minkowitz, Alan C Legasto, Joanna G Escalon, Sharon Steinberger, Mark Bittman, Thomas C Shen, Ying Ding, Ronald M Summers, George Shih, Yifan Peng, Zhangyang Wang

{"title":"How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?","authors":"Gregory Holste, Ziyu Jiang, Ajay Jaiswal, Maria Hanna, Shlomo Minkowitz, Alan C Legasto, Joanna G Escalon, Sharon Steinberger, Mark Bittman, Thomas C Shen, Ying Ding, Ronald M Summers, George Shih, Yifan Peng, Zhangyang Wang","doi":"10.1007/978-3-031-43904-9_64","DOIUrl":"10.1007/978-3-031-43904-9_64","url":null,"abstract":"Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class \"forgettability\" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://github.com/VITA-Group/PruneCXR.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14224 ","pages":"663-673"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10568970/pdf/nihms-1936096.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41224575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One-shot Federated Learning on Medical Data using Knowledge Distillation with Image Synthesis and Client Model Adaptation. 利用知识蒸馏、图像合成和客户端模型适配对医疗数据进行一次性联合学习。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43895-0_49

Myeongkyun Kang, Philip Chikontwe, Soopil Kim, Kyong Hwan Jin, Ehsan Adeli, Kilian M Pohl, Sang Hyun Park

{"title":"One-shot Federated Learning on Medical Data using Knowledge Distillation with Image Synthesis and Client Model Adaptation.","authors":"Myeongkyun Kang, Philip Chikontwe, Soopil Kim, Kyong Hwan Jin, Ehsan Adeli, Kilian M Pohl, Sang Hyun Park","doi":"10.1007/978-3-031-43895-0_49","DOIUrl":"10.1007/978-3-031-43895-0_49","url":null,"abstract":"One-shot federated learning (FL) has emerged as a promising solution in scenarios where multiple communication rounds are not practical. Notably, as feature distributions in medical data are less discriminative than those of natural images, robust global model training with FL is non-trivial and can lead to overfitting. To address this issue, we propose a novel one-shot FL framework leveraging Image Synthesis and Client model Adaptation (FedISCA) with knowledge distillation (KD). To prevent overfitting, we generate diverse synthetic images ranging from random noise to realistic images. This approach (i) alleviates data privacy concerns and (ii) facilitates robust global model training using KD with decentralized client models. To mitigate domain disparity in the early stages of synthesis, we design noise-adapted client models where batch normalization statistics on random noise (synthetic images) are updated to enhance KD. Lastly, the global model is trained with both the original and noise-adapted client models via KD and synthetic images. This process is repeated till global model convergence. Extensive evaluation of this design on five small- and three large-scale medical image classification datasets reveals superior accuracy over prior methods. Code is available at https://github.com/myeongkyunkang/FedISCA.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14221 ","pages":"521-531"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10781197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139418907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection. 拉普拉斯变换器克服视觉变换器在局部纹理检测中的局限性

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43898-1_70

Reza Azad, Amirhossein Kazerouni, Babak Azad, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci, Dorit Merhof

{"title":"Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection.","authors":"Reza Azad, Amirhossein Kazerouni, Babak Azad, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci, Dorit Merhof","doi":"10.1007/978-3-031-43898-1_70","DOIUrl":"10.1007/978-3-031-43898-1_70","url":null,"abstract":"Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it has been observed that the ViT models struggle to capture high-frequency components of images, which can limit their ability to detect local textures and edge information. As abnormalities in human tissue, such as tumors and lesions, may greatly vary in structure, texture, and shape, high-frequency information such as texture is crucial for effective semantic segmentation tasks. To address this limitation in ViT models, we propose a new technique, Laplacian-Former, that enhances the self-attention map by adaptively re-calibrating the frequency information in a Laplacian pyramid. More specifically, our proposed method utilizes a dual attention mechanism via efficient attention and frequency attention while the efficient attention mechanism reduces the complexity of self-attention to linear while producing the same output, selectively intensifying the contribution of shape and texture features. Furthermore, we introduce a novel efficient enhancement multi-scale bridge that effectively transfers spatial information from the encoder to the decoder while preserving the fundamental features. We demonstrate the efficacy of Laplacian-former on multi-organ and skin lesion segmentation tasks with +1.87% and +0.76% dice scores compared to SOTA approaches, respectively. Our implementation is publically available at GitHub.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14222 ","pages":"736-746"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10830169/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139652500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cochlear Implant Fold Detection in Intra-operative CT Using Weakly Supervised Multi-task Deep Learning. 利用弱监督多任务深度学习在术中 CT 中检测人工耳蜗褶皱

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43996-4_24

Mohammad M R Khan, Yubo Fan, Benoit M Dawant, Jack H Noble

{"title":"Cochlear Implant Fold Detection in Intra-operative CT Using Weakly Supervised Multi-task Deep Learning.","authors":"Mohammad M R Khan, Yubo Fan, Benoit M Dawant, Jack H Noble","doi":"10.1007/978-3-031-43996-4_24","DOIUrl":"10.1007/978-3-031-43996-4_24","url":null,"abstract":"In cochlear implant (CI) procedures, an electrode array is surgically inserted into the cochlea. The electrodes are used to stimulate the auditory nerve and restore hearing sensation for the recipient. If the array folds inside the cochlea during the insertion procedure, it can lead to trauma, damage to the residual hearing, and poor hearing restoration. Intraoperative detection of such a case can allow a surgeon to perform reimplantation. However, this intraoperative detection requires experience and electrophysiological tests sometimes fail to detect an array folding. Due to the low incidence of array folding, we generated a dataset of CT images with folded synthetic electrode arrays with realistic metal artifact. The dataset was used to train a multitask custom 3D-UNet model for array fold detection. We tested the trained model on real post-operative CTs (7 with folded arrays and 200 without). Our model could correctly classify all the fold-over cases while misclassifying only 3 non fold-over cases. Therefore, the model is a promising option for array fold detection.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14228 ","pages":"249-259"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10953791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0