Information processing in medical imaging : proceedings of the ... conference最新文献_第3页

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

NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces 预测变形空间测地线的神经算子

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-03-13 DOI: 10.48550/arXiv.2303.07115

Nian Wu, Miaomiao Zhang

{"title":"NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces","authors":"Nian Wu, Miaomiao Zhang","doi":"10.48550/arXiv.2303.07115","DOIUrl":"https://doi.org/10.48550/arXiv.2303.07115","url":null,"abstract":"This paper presents NeurEPDiff, a novel network to fast predict the geodesics in deformation spaces generated by a well known Euler-Poincar'e differential equation (EPDiff). To achieve this, we develop a neural operator that for the first time learns the evolving trajectory of geodesic deformations parameterized in the tangent space of diffeomorphisms(a.k.a velocity fields). In contrast to previous methods that purely fit the training images, our proposed NeurEPDiff learns a nonlinear mapping function between the time-dependent velocity fields. A composition of integral operators and smooth activation functions is formulated in each layer of NeurEPDiff to effectively approximate such mappings. The fact that NeurEPDiff is able to rapidly provide the numerical solution of EPDiff (given any initial condition) results in a significantly reduced computational cost of geodesic shooting of diffeomorphisms in a high-dimensional image space. Additionally, the properties of discretiztion/resolution-invariant of NeurEPDiff make its performance generalizable to multiple image resolutions after being trained offline. We demonstrate the effectiveness of NeurEPDiff in registering two image datasets: 2D synthetic data and 3D brain resonance imaging (MRI). The registration accuracy and computational efficiency are compared with the state-of-the-art diffeomophic registration algorithms with geodesic shooting.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"24 1","pages":"588-600"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79122410","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: 3

A Surface-normal Based Neural Framework for Colonoscopy Reconstruction 基于表面正常的结肠镜重建神经框架

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-03-13 DOI: 10.48550/arXiv.2303.07264

Shuxian Wang, Yubo Zhang, Sarah K. McGill, J. Rosenman, Jan-Michael Frahm, Soumyadip Sengupta, S. Pizer

{"title":"A Surface-normal Based Neural Framework for Colonoscopy Reconstruction","authors":"Shuxian Wang, Yubo Zhang, Sarah K. McGill, J. Rosenman, Jan-Michael Frahm, Soumyadip Sengupta, S. Pizer","doi":"10.48550/arXiv.2303.07264","DOIUrl":"https://doi.org/10.48550/arXiv.2303.07264","url":null,"abstract":"Reconstructing a 3D surface from colonoscopy video is challenging due to illumination and reflectivity variation in the video frame that can cause defective shape predictions. Aiming to overcome this challenge, we utilize the characteristics of surface normal vectors and develop a two-step neural framework that significantly improves the colonoscopy reconstruction quality. The normal-based depth initialization network trained with self-supervised normal consistency loss provides depth map initialization to the normal-depth refinement module, which utilizes the relationship between illumination and surface normals to refine the frame-wise normal and depth predictions recursively. Our framework's depth accuracy performance on phantom colonoscopy data demonstrates the value of exploiting the surface normals in colonoscopy reconstruction, especially on en face views. Due to its low depth error, the prediction result from our framework will require limited post-processing to be clinically applicable for real-time colonoscopy reconstruction.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"30 1","pages":"797-809"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91340772","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: 1

Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease 不要惊慌:用于阿尔茨海默病可解释分类的原型加法神经网络

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-03-13 DOI: 10.48550/arXiv.2303.07125

Thomas Wolf, Sebastian Pölsterl, C. Wachinger

{"title":"Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease","authors":"Thomas Wolf, Sebastian Pölsterl, C. Wachinger","doi":"10.48550/arXiv.2303.07125","DOIUrl":"https://doi.org/10.48550/arXiv.2303.07125","url":null,"abstract":"Alzheimer's disease (AD) has a complex and multifactorial etiology, which requires integrating information about neuroanatomy, genetics, and cerebrospinal fluid biomarkers for accurate diagnosis. Hence, recent deep learning approaches combined image and tabular information to improve diagnostic performance. However, the black-box nature of such neural networks is still a barrier for clinical applications, in which understanding the decision of a heterogeneous model is integral. We propose PANIC, a prototypical additive neural network for interpretable AD classification that integrates 3D image and tabular data. It is interpretable by design and, thus, avoids the need for post-hoc explanations that try to approximate the decision of a network. Our results demonstrate that PANIC achieves state-of-the-art performance in AD classification, while directly providing local and global explanations. Finally, we show that PANIC extracts biologically meaningful signatures of AD, and satisfies a set of desirable desiderata for trustworthy machine learning. Our implementation is available at https://github.com/ai-med/PANIC .","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"28 1","pages":"82-94"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76432496","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: 1

Token Sparsification for Faster Medical Image Segmentation 用于快速医学图像分割的标记稀疏化

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-03-11 DOI: 10.48550/arXiv.2303.06522

Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, D. Samaras, P. Prasanna

{"title":"Token Sparsification for Faster Medical Image Segmentation","authors":"Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, D. Samaras, P. Prasanna","doi":"10.48550/arXiv.2303.06522","DOIUrl":"https://doi.org/10.48550/arXiv.2303.06522","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 naively 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.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"19 1","pages":"743-754"},"PeriodicalIF":0.0,"publicationDate":"2023-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84304658","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

Resolving quantitative MRI model degeneracy with machine learning via training data distribution design 通过训练数据分布设计，用机器学习解决定量MRI模型退化问题

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-03-09 DOI: 10.48550/arXiv.2303.05464

Michele Guerreri, Sean C. Epstein, H. Azadbakht, Hui Zhang

{"title":"Resolving quantitative MRI model degeneracy with machine learning via training data distribution design","authors":"Michele Guerreri, Sean C. Epstein, H. Azadbakht, Hui Zhang","doi":"10.48550/arXiv.2303.05464","DOIUrl":"https://doi.org/10.48550/arXiv.2303.05464","url":null,"abstract":"Quantitative MRI (qMRI) aims to map tissue properties non-invasively via models that relate these unknown quantities to measured MRI signals. Estimating these unknowns, which has traditionally required model fitting - an often iterative procedure, can now be done with one-shot machine learning (ML) approaches. Such parameter estimation may be complicated by intrinsic qMRI signal model degeneracy: different combinations of tissue properties produce the same signal. Despite their many advantages, it remains unclear whether ML approaches can resolve this issue. Growing empirical evidence appears to suggest ML approaches remain susceptible to model degeneracy. Here we demonstrate under the right circumstances ML can address this issue. Inspired by recent works on the impact of training data distributions on ML-based parameter estimation, we propose to resolve model degeneracy by designing training data distributions. We put forward a classification of model degeneracies and identify one particular kind of degeneracies amenable to the proposed attack. The strategy is demonstrated successfully using the Revised NODDI model with standard multi-shell diffusion MRI data as an exemplar. Our results illustrate the importance of training set design which has the potential to allow accurate estimation of tissue properties with ML.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"3 1","pages":"3-14"},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86424637","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

MetaMorph: Learning Metamorphic Image Transformation With Appearance Changes 变形:学习变形图像的外观变化

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-03-08 DOI: 10.48550/arXiv.2303.04849

Jian Wang, Jiarui Xing, J.T. Druzgal, W. Wells, Miaomiao Zhang

{"title":"MetaMorph: Learning Metamorphic Image Transformation With Appearance Changes","authors":"Jian Wang, Jiarui Xing, J.T. Druzgal, W. Wells, Miaomiao Zhang","doi":"10.48550/arXiv.2303.04849","DOIUrl":"https://doi.org/10.48550/arXiv.2303.04849","url":null,"abstract":"This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i.e., caused by brain tumors). In contrast to previous learning-based registration methods that have little or no control over appearance-changes, our model introduces a new regularization that can effectively suppress the negative effects of appearance changing areas. In particular, we develop a piecewise regularization on the tangent space of diffeomorphic transformations (also known as initial velocity fields) via learned segmentation maps of abnormal regions. The geometric transformation and appearance changes are treated as joint tasks that are mutually beneficial. Our model MetaMorph is more robust and accurate when searching for an optimal registration solution under the guidance of segmentation, which in turn improves the segmentation performance by providing appropriately augmented training labels. We validate MetaMorph on real 3D human brain tumor magnetic resonance imaging (MRI) scans. Experimental results show that our model outperforms the state-of-the-art learning-based registration models. The proposed MetaMorph has great potential in various image-guided clinical interventions, e.g., real-time image-guided navigation systems for tumor removal surgery.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"67 1","pages":"576-587"},"PeriodicalIF":0.0,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83589864","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: 1

Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace's Equation 基于拉普拉斯方程的深度学习框架改进皮层灰质深沟分割

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-03-01 DOI: 10.48550/arXiv.2303.00795

S. Ravikumar, Ranjit Itttyerah, Sydney A. Lim, L. Xie, Sandhitsu R. Das, Pulkit Khandelwal, L. Wisse, M. Bedard, John L. Robinson, Terry K. Schuck, M. Grossman, J. Trojanowski, Eddie B. Lee, M. Tisdall, K. Prabhakaran, J. Detre, D. Irwin, Winifred Trotman, G. Mizsei, Emilio Artacho-P'erula, Maria Mercedes Iniguez de Onzono Martin, Maria del Mar Arroyo Jim'enez, M. Muñoz, Francisco Javier Molina Romero, M. Rabal, Sandra Cebada-S'anchez, J. Gonz'alez, C. Rosa-Prieto, Marta Córcoles Parada, D. Wolk, R. Insausti, Paul Yushkevich

{"title":"Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace's Equation","authors":"S. Ravikumar, Ranjit Itttyerah, Sydney A. Lim, L. Xie, Sandhitsu R. Das, Pulkit Khandelwal, L. Wisse, M. Bedard, John L. Robinson, Terry K. Schuck, M. Grossman, J. Trojanowski, Eddie B. Lee, M. Tisdall, K. Prabhakaran, J. Detre, D. Irwin, Winifred Trotman, G. Mizsei, Emilio Artacho-P'erula, Maria Mercedes Iniguez de Onzono Martin, Maria del Mar Arroyo Jim'enez, M. Muñoz, Francisco Javier Molina Romero, M. Rabal, Sandra Cebada-S'anchez, J. Gonz'alez, C. Rosa-Prieto, Marta Córcoles Parada, D. Wolk, R. Insausti, Paul Yushkevich","doi":"10.48550/arXiv.2303.00795","DOIUrl":"https://doi.org/10.48550/arXiv.2303.00795","url":null,"abstract":"When developing tools for automated cortical segmentation, the ability to produce topologically correct segmentations is important in order to compute geometrically valid morphometry measures. In practice, accurate cortical segmentation is challenged by image artifacts and the highly convoluted anatomy of the cortex itself. To address this, we propose a novel deep learning-based cortical segmentation method in which prior knowledge about the geometry of the cortex is incorporated into the network during the training process. We design a loss function which uses the theory of Laplace's equation applied to the cortex to locally penalize unresolved boundaries between tightly folded sulci. Using an ex vivo MRI dataset of human medial temporal lobe specimens, we demonstrate that our approach outperforms baseline segmentation networks, both quantitatively and qualitatively.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"146 1","pages":"692-704"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77647104","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