Fully Automated Detection and Segmentation Pipeline for the Bone Marrow of the Lytic Bone of Multiple Myeloma Patients
文献类型： 会议论文
第一作者： Nickolas Papanikolaou
作者： Nickolas Papanikolaou 1 ; Dimitrios I. Fotiadis 2 ; Kostas Marias 3 ; Emmanouil Koutoulakis 3 ; Eleftherios Trivizakis 3 ; Vassilis Koutoulidis 4 ; Lia A. Moulopoulos 4 ; Evangelos Terpos 5 ; Ioannis Ntanasis-Stathopoulos 5 ; Panagiotis Malandrakis 5 ; Panagiotis Grigoropoulos 4 ; Panagiotis Papadopoulos 4 ; Katerina Nikiforaki 3 ;
作者机构： 1.Computational Clinical Imaging Group, Centre of the Unknown, Champalimaud Foundation, Lisbon, Portugal
2.Department of MaterialsScience and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina
3.Foundation for Research and Technology – Hellas, Heraklion, Greece
4.1st Department of Radiology, School of Medicine, Aretaieion Hospital, National Kapodistrian University of Athens, Athens, Greece
5.Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
关键词： Patient monitoring;Pipelines;Medical treatment;Bones;Robustness;Lesions;Task analysis
会议名称： IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology
主办单位：
页码： 39-40
摘要： Monitoring the changes in bone marrow during therapy for multiple myeloma patients is a crucial task. Osteolytic lesions can cause deformation of the bones, affecting the robustness of traditional segmentation tools. A two-model deep learning analysis is explored in this study. A detection model reduces pixel imbalances between the background and the bone marrow pixels, achieving a mAP of 0.878±0.005. A residual U-Net segments the bone marrow, yielding a DSC of 0.856±0.003. The proposed deep learning-based segmentation pipeline allows accurate and fast annotation of the bone marrow in multiple myeloma patients.Clinical Relevance: The proposed deep learning-based pipeline for segmentation has the potential to fully automate the time-consuming process of delineating bone marrow in multiple myeloma patients, significantly improving patient monitoring.
分类号： r318-53
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Ensemble of Heterogeneous Machine Learning Models with Multiple Inputs for Multi-Omics Analysis
作者：Eleftherios Trivizakis;Vassilis Koutoulidis;Lia A. Moulopoulos;Evangelos Terpos;Ioannis Ntanasis-Stathopoulos;Panagiotis Malandrakis;Panagiotis Grigoropoulos;Panagiotis Papadopoulos;Katerina Nikiforaki;Kostas Marias;Nickolas Papanikolaou
关键词：Biological system modeling;Transfer learning;Genomics;Medical services;Plasmas;Ensemble learning;Neoplasms
