Information processing in medical imaging : proceedings of the ... conference最新文献_第2页

MeshDeform: Surface Reconstruction of Subcortical Structures in Human Brain MRI. 网格变形：人脑MRI皮层下结构的表面重建。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 Epub Date: 2023-06-08 DOI: 10.1007/978-3-031-34048-2_41

Junjie Zhao, Siyuan Liu, Sahar Ahmad, Yap Pew-Thian

{"title":"MeshDeform: Surface Reconstruction of Subcortical Structures in Human Brain MRI.","authors":"Junjie Zhao, Siyuan Liu, Sahar Ahmad, Yap Pew-Thian","doi":"10.1007/978-3-031-34048-2_41","DOIUrl":"10.1007/978-3-031-34048-2_41","url":null,"abstract":"Surface reconstruction of cortical and subcortical structures is crucial for brain morphological studies. Existing deep learning surface reconstruction methods, such as DeepCSR and Vox2Surf, learn an implicit field function for computing the isosurface, but do not consider mesh topology. In this paper, we propose a novel and efficient deep learning mesh deformation network, called MeshDeform, to reconstruct topologically correct surfaces of subcortical structures using brain MR images. MeshDeform combines features extracted from a U-Net encoder with mesh deformation blocks to predict surfaces of subcortical structures by deforming spherical mesh templates. MeshDeform is able to reconstruct in less than 10 seconds the surfaces of a left-right pair of subcortical structures with subvoxel accuracy. Reconstruction of all 17 subcortical structures takes less than one and a half minutes. By contrast, Vox2Surf takes about 20-30 minutes for all subcortical structures. Visual and quantitative evaluation on the Human Connectome Project (HCP) dataset demonstrate that MeshDeform generates accurate subcortical surfaces in limited time while preserving mesh topology.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 ","pages":"536-547"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71429784","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

Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-Aware Contrastive Distillation. 利用解剖感知对比蒸馏技术引导半监督医学图像分割。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 Epub Date: 2023-06-08 DOI: 10.1007/978-3-031-34048-2_49

Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S Duncan

{"title":"Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-Aware Contrastive Distillation.","authors":"Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S Duncan","doi":"10.1007/978-3-031-34048-2_49","DOIUrl":"10.1007/978-3-031-34048-2_49","url":null,"abstract":"Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for both labeled and unlabeled medical images. However, medical image data in reality is commonly imbalanced (i.e., multi-class label imbalance), which naturally yields blurry contours and usually incorrectly labels rare objects. Moreover, it remains unclear whether all negative samples are equally negative. In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation. Specifically, we first develop an iterative contrastive distillation algorithm by softly labeling the negatives rather than binary supervision between positive and negative pairs. We also capture more semantically similar features from the randomly chosen negative set compared to the positives to enforce the diversity of the sampled data. Second, we raise a more important question: Can we really handle imbalanced samples to yield better performance? Hence, the key innovation in ACTION is to learn global semantic relationship across the entire dataset and local anatomical features among the neighbouring pixels with minimal additional memory footprint. During the training, we introduce anatomical contrast by actively sampling a sparse set of hard negative pixels, which can generate smoother segmentation boundaries and more accurate predictions. Extensive experiments across two benchmark datasets and different unlabeled settings show that ACTION significantly outperforms the current state-of-the-art semi-supervised methods.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 ","pages":"641-653"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322187/pdf/nihms-1913000.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9794875","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

OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing. OTRE:其中最优传输引导的非配对图像到图像的翻译满足正则化通过增强。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 DOI: 10.1007/978-3-031-34048-2_32

Wenhui Zhu, Peijie Qiu, Oana M Dumitrascu, Jacob M Sobczak, Mohammad Farazi, Zhangsihao Yang, Keshav Nandakumar, Yalin Wang

{"title":"OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing.","authors":"Wenhui Zhu, Peijie Qiu, Oana M Dumitrascu, Jacob M Sobczak, Mohammad Farazi, Zhangsihao Yang, Keshav Nandakumar, Yalin Wang","doi":"10.1007/978-3-031-34048-2_32","DOIUrl":"https://doi.org/10.1007/978-3-031-34048-2_32","url":null,"abstract":"Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts. Furthermore, to improve the flexibility, robustness, and applicability of our image enhancement pipeline in the clinical practice, we generalized a state-of-the-art model-based image reconstruction method, regularization by denoising, by plugging in priors learned by our OT-guided image-to-image translation network. We named it as regularization by enhancing (RE). We validated the integrated framework, OTRE, on three publicly available retinal image datasets by assessing the quality after enhancement and their performance on various downstream tasks, including diabetic retinopathy grading, vessel segmentation, and diabetic lesion segmentation. The experimental results demonstrated the superiority of our proposed framework over some state-of-the-art unsupervised competitors and a state-of-the-art supervised method.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 ","pages":"415-427"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10329768/pdf/nihms-1880857.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9811952","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

Token Sparsification for Faster Medical Image Segmentation. 令牌稀疏化实现更快的医学图像分割

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 Epub Date: 2023-06-08 DOI: 10.1007/978-3-031-34048-2_57

Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, Prateek Prasanna

{"title":"Token Sparsification for Faster Medical Image Segmentation.","authors":"Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, Prateek Prasanna","doi":"10.1007/978-3-031-34048-2_57","DOIUrl":"https://doi.org/10.1007/978-3-031-34048-2_57","url":null,"abstract":"Can we use sparse tokens for dense prediction, e.g., segmentation? Although token sparsification has been applied to Vision Transformers (ViT) to accelerate classification, it is still unknown how to perform segmentation from sparse tokens. To this end, we reformulate segmentation as a sparse encoding → token completion → dense decoding (SCD) pipeline. We first empirically show that naïvely applying existing approaches from classification token pruning and masked image modeling (MIM) leads to failure and inefficient training caused by inappropriate sampling algorithms and the low quality of the restored dense features. In this paper, we propose Soft-topK Token Pruning (STP) and Multi-layer Token Assembly (MTA) to address these problems. In sparse encoding, STP predicts token importance scores with a lightweight sub-network and samples the topK tokens. The intractable topK gradients are approximated through a continuous perturbed score distribution. In token completion, MTA restores a full token sequence by assembling both sparse output tokens and pruned multi-layer intermediate ones. The last dense decoding stage is compatible with existing segmentation decoders, e.g., UNETR. Experiments show SCD pipelines equipped with STP and MTA are much faster than baselines without token pruning in both training (up to 120% higher throughput) and inference (up to 60.6% higher throughput) while maintaining segmentation quality. Code is available here: https://github.com/cvlab-stonybrook/TokenSparse-for-MedSeg.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 ","pages":"743-754"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140856074","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

Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data. 通过 Hodge-Laplacian 处理大脑功能数据的异构图卷积神经网络

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 Epub Date: 2023-06-08 DOI: 10.1007/978-3-031-34048-2_22

Jinghan Huang, Moo K Chung, Anqi Qiu

{"title":"Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data.","authors":"Jinghan Huang, Moo K Chung, Anqi Qiu","doi":"10.1007/978-3-031-34048-2_22","DOIUrl":"10.1007/978-3-031-34048-2_22","url":null,"abstract":"This study proposes a novel heterogeneous graph convolutional neural network (HGCNN) to handle complex brain fMRI data at regional and across-region levels. We introduce a generic formulation of spectral filters on heterogeneous graphs by introducing the k-th Hodge-Laplacian (HL) operator. In particular, we propose Laguerre polynomial approximations of HL spectral filters and prove that their spatial localization on graphs is related to the polynomial order. Furthermore, based on the bijection property of boundary operators on simplex graphs, we introduce a generic topological graph pooling (TGPool) method that can be used at any dimensional simplices. This study designs HL-node, HL-edge, and HL-HGCNN neural networks to learn signal representation at a graph node, edge levels, and both, respectively. Our experiments employ fMRI from the Adolescent Brain Cognitive Development (ABCD; n=7693) to predict general intelligence. Our results demonstrate the advantage of the HL-edge network over the HL-node network when functional brain connectivity is considered as features. The HL-HGCNN outperforms the state-of-the-art graph neural networks (GNNs) approaches, such as GAT, BrainGNN, dGCN, BrainNetCNN, and Hypergraph NN. The functional connectivity features learned from the HL-HGCNN are meaningful in interpreting neural circuits related to general intelligence.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 ","pages":"278-290"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11108189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077300","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

Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation Mixup-Privacy:一种简单而有效的隐私保护分割方法

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-05-23 DOI: 10.48550/arXiv.2305.13756

B. Kim, J. Dolz, Pierre-Marc Jodoin, Christian Desrosiers

{"title":"Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation","authors":"B. Kim, J. Dolz, Pierre-Marc Jodoin, Christian Desrosiers","doi":"10.48550/arXiv.2305.13756","DOIUrl":"https://doi.org/10.48550/arXiv.2305.13756","url":null,"abstract":"Privacy protection in medical data is a legitimate obstacle for centralized machine learning applications. Here, we propose a client-server image segmentation system which allows for the analysis of multi-centric medical images while preserving patient privacy. In this approach, the client protects the to-be-segmented patient image by mixing it to a reference image. As shown in our work, it is challenging to separate the image mixture to exact original content, thus making the data unworkable and unrecognizable for an unauthorized person. This proxy image is sent to a server for processing. The server then returns the mixture of segmentation maps, which the client can revert to a correct target segmentation. Our system has two components: 1) a segmentation network on the server side which processes the image mixture, and 2) a segmentation unmixing network which recovers the correct segmentation map from the segmentation mixture. Furthermore, the whole system is trained end-to-end. The proposed method is validated on the task of MRI brain segmentation using images from two different datasets. Results show that the segmentation accuracy of our method is comparable to a system trained on raw images, and outperforms other privacy-preserving methods with little computational overhead.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"40 1","pages":"717-729"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83483216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation 医学图像分割深度学习模型中边界检测的再思考

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-05-01 DOI: 10.48550/arXiv.2305.00678

Yi-Mou Lin, Dong-Ming Zhang, Xiaori Fang, Yufan Chen, Kwang-Ting Cheng, Hao Chen

{"title":"Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation","authors":"Yi-Mou Lin, Dong-Ming Zhang, Xiaori Fang, Yufan Chen, Kwang-Ting Cheng, Hao Chen","doi":"10.48550/arXiv.2305.00678","DOIUrl":"https://doi.org/10.48550/arXiv.2305.00678","url":null,"abstract":"Medical image segmentation is a fundamental task in the community of medical image analysis. In this paper, a novel network architecture, referred to as Convolution, Transformer, and Operator (CTO), is proposed. CTO employs a combination of Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and an explicit boundary detection operator to achieve high recognition accuracy while maintaining an optimal balance between accuracy and efficiency. The proposed CTO follows the standard encoder-decoder segmentation paradigm, where the encoder network incorporates a popular CNN backbone for capturing local semantic information, and a lightweight ViT assistant for integrating long-range dependencies. To enhance the learning capacity on boundary, a boundary-guided decoder network is proposed that uses a boundary mask obtained from a dedicated boundary detection operator as explicit supervision to guide the decoding learning process. The performance of the proposed method is evaluated on six challenging medical image segmentation datasets, demonstrating that CTO achieves state-of-the-art accuracy with a competitive model complexity.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"77 1","pages":"730-742"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87056818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding 使用无监督嵌入的基于实时图像的神经外科指导和路线图生成

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-03-31 DOI: 10.48550/arXiv.2303.18019

Gary Sarwin, A. Carretta, V. Staartjes, M. Zoli, D. Mazzatenta, L. Regli, C. Serra, E. Konukoglu

{"title":"Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding","authors":"Gary Sarwin, A. Carretta, V. Staartjes, M. Zoli, D. Mazzatenta, L. Regli, C. Serra, E. Konukoglu","doi":"10.48550/arXiv.2303.18019","DOIUrl":"https://doi.org/10.48550/arXiv.2303.18019","url":null,"abstract":"Advanced minimally invasive neurosurgery navigation relies mainly on Magnetic Resonance Imaging (MRI) guidance. MRI guidance, however, only provides pre-operative information in the majority of the cases. Once the surgery begins, the value of this guidance diminishes to some extent because of the anatomical changes due to surgery. Guidance with live image feedback coming directly from the surgical device, e.g., endoscope, can complement MRI-based navigation or be an alternative if MRI guidance is not feasible. With this motivation, we present a method for live image-only guidance leveraging a large data set of annotated neurosurgical videos.First, we report the performance of a deep learning-based object detection method, YOLO, on detecting anatomical structures in neurosurgical images. Second, we present a method for generating neurosurgical roadmaps using unsupervised embedding without assuming exact anatomical matches between patients, presence of an extensive anatomical atlas, or the need for simultaneous localization and mapping. A generated roadmap encodes the common anatomical paths taken in surgeries in the training set. At inference, the roadmap can be used to map a surgeon's current location using live image feedback on the path to provide guidance by being able to predict which structures should appear going forward or backward, much like a mapping application. Even though the embedding is not supervised by position information, we show that it is correlated to the location inside the brain and on the surgical path. We trained and evaluated the proposed method with a data set of 166 transsphenoidal adenomectomy procedures.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"12 1","pages":"107-118"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82443376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images 生物医学单细胞图像中多实例学习模型的像素级解释

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-03-15 DOI: 10.48550/arXiv.2303.08632

A. Sadafi, Oleksandra Adonkina, Ashkan Khakzar, P. Lienemann, Rudolf Matthias Hehr, D. Rueckert, N. Navab, C. Marr

{"title":"Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images","authors":"A. Sadafi, Oleksandra Adonkina, Ashkan Khakzar, P. Lienemann, Rudolf Matthias Hehr, D. Rueckert, N. Navab, C. Marr","doi":"10.48550/arXiv.2303.08632","DOIUrl":"https://doi.org/10.48550/arXiv.2303.08632","url":null,"abstract":"Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making. Multiple instance learning with attention pooling provides instance-level explainability, however for many clinical applications a deeper, pixel-level explanation is desirable, but missing so far. In this work, we investigate the use of four attribution methods to explain a multiple instance learning models: GradCAM, Layer-Wise Relevance Propagation (LRP), Information Bottleneck Attribution (IBA), and InputIBA. With this collection of methods, we can derive pixel-level explanations on for the task of diagnosing blood cancer from patients' blood smears. We study two datasets of acute myeloid leukemia with over 100 000 single cell images and observe how each attribution method performs on the multiple instance learning architecture focusing on different properties of the white blood single cells. Additionally, we compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard. Our study addresses the challenge of implementing pixel-level explainability in multiple instance learning models and provides insights for clinicians to better understand and trust decisions from computer-aided diagnosis systems.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"23 1","pages":"170-182"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78891836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery 光晕:器官切除术后无幻觉的器官分割

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-03-14 DOI: 10.48550/arXiv.2303.07717

Anne-Marie Rickmann, Murong Xu, Thomas Wolf, Oksana P. Kovalenko, C. Wachinger

{"title":"HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery","authors":"Anne-Marie Rickmann, Murong Xu, Thomas Wolf, Oksana P. Kovalenko, C. Wachinger","doi":"10.48550/arXiv.2303.07717","DOIUrl":"https://doi.org/10.48550/arXiv.2303.07717","url":null,"abstract":"The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications. A clinically relevant problem that received less attention is the handling of scans with irregular anatomy, e.g., after organ resection. State-of-the-art segmentation models often lead to organ hallucinations, i.e., false-positive predictions of organs, which cannot be alleviated by oversampling or post-processing. Motivated by the increasing need to develop robust deep learning models, we propose HALOS for abdominal organ segmentation in MR images that handles cases after organ resection surgery. To this end, we combine missing organ classification and multi-organ segmentation tasks into a multi-task model, yielding a classification-assisted segmentation pipeline. The segmentation network learns to incorporate knowledge about organ existence via feature fusion modules. Extensive experiments on a small labeled test set and large-scale UK Biobank data demonstrate the effectiveness of our approach in terms of higher segmentation Dice scores and near-to-zero false positive prediction rate.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"12 1","pages":"667-678"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76323908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0