Geert Litjens
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    • Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
    • Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation.
    • Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps.
    • Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology.
    • The Medical Segmentation Decathlon
    • Deep learning in histopathology: the path to the clinic.
    • Residual cyclegan for robust domain transformation of histopathological tissue slides.
    • Common Limitations of Image Processing Metrics: A Picture Story
    • Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics.
    • Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels.
    • Neural Image Compression for Gigapixel Histopathology Image Analysis.
    • Common limitations of performance metrics in biomedical image analysis
    • End-to-end classification on basal-cell carcinoma histopathology whole-slides images
    • Tailoring automated data augmentation to H&E-stained histopathology
    • Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer
    • Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists
    • Streaming convolutional neural networks for end-to-end learning with multi-megapixel images
    • The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma.
    • Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma
    • Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study
    • Common limitations of performance metrics in biomedical image analysis
    • Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks
    • Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images
    • Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks
    • No pixel-level annotations needed
    • Learning to detect lymphocytes in immunohistochemistry with deep learning
    • Neural Image Compression for Gigapixel Histopathology Image Analysis
    • Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.
    • State-of-the-Art Deep Learning in Cardiovascular Image Analysis.
    • A large annotated medical image dataset for the development and evaluation of segmentation algorithms
    • Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
    • A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach.
    • Automated Gleason Grading of Prostate Biopsies Using Deep Learning
    • Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
    • Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification
    • High resolution whole prostate biopsy classification using streaming stochastic gradient descent
    • Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks
    • Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks
    • Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology
    • From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge
    • 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
    • Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
    • Automated segmentation of epithelial tissue in prostatectomy slides using deep learning
    • Automatic color unmixing of IHC stained whole slide images
    • H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection
    • Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images
    • Training convolutional neural networks with megapixel images
    • Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders
    • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
    • Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
    • Evaluation of tongue squamous cell carcinoma resection margins using ex-vivo MR.
    • Comparison of Different Methods for Tissue Segmentation In Histopathological Whole-Slide Images
    • The importance of stain normalization in colorectal tissue classification with convolutional networks
    • Using deep learning to segment breast and fibroglandular tissue in MRI volumes
    • A Survey on Deep Learning in Medical Image Analysis
    • Automatic segmentation of histopathological slides from renal allograft biopsies using artificial intelligence
    • Large scale deep learning for computer aided detection of mammographic lesions
    • Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
    • MAGE expression in head and neck squamous cell carcinoma primary tumors, lymph node metastases and respective recurrences: implications for immunotherapy
    • Intranodal signal suppression in pelvic MR lymphography of prostate cancer patients: a quantitative comparison of ferumoxtran-10 and ferumoxytol.
    • Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images
    • Stain specific standardization of whole-slide histopathological images
    • Automated multistructure atlas-assisted detection of lymph nodes using pelvic MR lymphography in prostate cancer patients
    • In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide
    • Automated robust registration of grossly misregistered whole-slide images with varying stains
    • Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.
    • Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
    • Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks
    • Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI
    • Multiparametric Magnetic Resonance Imaging for Discriminating Low-Grade From High-Grade Prostate Cancer
    • A multi-scale superpixel classification approach for region of interest detection in whole slide histopathology images
    • Automated detection of prostate cancer in digitized whole-slide images of H&E-stained biopsy specimens
    • Computerized detection of cancer in multi-parametric prostate MRI
    • Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge
    • Computer-aided detection of prostate cancer in MRI
    • Computer-aided Detection of Prostate Cancer in Multi-parametric Magnetic Resonance Imaging
    • Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation
    • Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI
    • Multiparametric MR imaging for the assessment of prostate cancer aggressiveness at 3 Tesla
    • Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy
    • Assessment of Prostate Cancer Aggressiveness Using Dynamic Contrast-enhanced Magnetic Resonance Imaging at 3 T
    • Differentiation of Prostatitis and Prostate Cancer by Using Diffusion-weighted MR Imaging and MR-guided Biopsy at 3 T
    • Initial prospective evaluation of the prostate imaging reporting and data standard (PI-RADS): Can it reduce unnecessary MR guided biopsies?
    • Prostate Cancer localization with a Multiparametric MR Approach (PCaMAP): initial results of a multi-center study
    • Interpatient Variation in Normal Peripheral Zone Apparent Diffusion Coefficient: Effect on the Prediction of Prostate Cancer Aggressiveness
    • Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach
    • A multi-atlas approach for prostate segmentation in MRI
    • A pattern recognition approach to zonal segmentation of the prostate on MRI
    • Computerized characterization of central gland lesions using texture and relaxation features from T2-weighted prostate MRI
    • Dynamic contrast enhanced MR imaging for the assessment of prostate cancer aggressiveness at 3T
    • Automated 3-Dimensional Segmentation of Pelvic Lymph Nodes in Magnetic Resonance Images
    • Automatic Computer Aided Detection of Abnormalities in Multi-Parametric Prostate MRI
    • Detection of Lymph Node Metastases with Ferumoxtran-10 vs Ferumoxytol
    • Differentiation of Normal Prostate Tissue, Prostatitis, and Prostate Cancer: Correlation between Diffusion-weighted Imaging and MR-guided Biopsy
    • Required accuracy of MR-US registration for prostate biopsies
    • Zone-specific Automatic Computer-aided Detection of Prostate Cancer in MRI
    • Computer aided detection of prostate cancer using T2W, DWI and DCE-MRI: methods and clinical applications
    • Pharmacokinetic models in clinical practice: what model to use for DCE-MRI of the breast?
    • Simulation of nodules and diffuse infiltrates in chest radiographs using CT templates
    • Training a Computer Aided Detection System with Simulated Lung Nodules in Chest Radiographs
    • Pharmacokinetic modeling in breast cancer MRI
    • T1 Quantification: Variable Flip Angle Method vs Use of Reference Phantom
    • Interview with Kijk on AI in Medical Imaging
    • NRC article on artificial intelligence in medicine
    • BNR Wetenschap Vandaag - Interview AI for prostate cancer grading
    • NOS Tech Podcast on AI for Gleason Grading
    • Interview in 'De zomeravond van...' van Omroep P&M
    • Report on Omroep Gelderland on 'De Nieuwe Mens'
    • BNR Wetenschap Vandaag - Zomercollege
    • BNR Beter - Interview
    • Interview with Prostate Cancer Patient Foundation
    • Report on Nieuwsuur about CAMELYON.
    • Interview Regional TV at Alpe Hu'Zes
    • Interview with Vox about FameLab
    • Visit Dutch Prostate Cancer Patient Foundation
    • Artificial Intelligence in Prostate Cancer Diagnostics
    • Applications of Machine Learning for Clinical Practice
    • Introduction to Deep Learning in Medical Imaging
    • Bessensap
    • Dag van de Pathologie
    • The Digital Doctor
    • PhD Thesis Defense

A multi-atlas approach for prostate segmentation in MRI

Jan 1, 2012·
G. Litjens
,
N. Karssemeijer
,
H. J. Huisman
· 0 min read
PDF Cite
Type
Conference paper
Publication
MICCAI Workshop: Prostate Cancer Imaging: The PROMISE12 Prostate Segmentation Challenge
Last updated on Jan 1, 2012
← Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach Feb 1, 2012
A pattern recognition approach to zonal segmentation of the prostate on MRI Jan 1, 2012 →

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