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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Yang, Bo
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (20/20 displayed)
- 2024Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction citations
- 2023Development and evaluation of conductive ultra-lightweight cementitious composites for smart and sustainable infrastructure applicationscitations
- 2023Shear performance of lightweight SCC composite beam internally reinforced with CFRP laminate stirrups and GFRP barscitations
- 2023Influence of crumbed rubber inclusion on spalling, microstructure, and mechanical behaviour of UHPC exposed to elevated temperaturescitations
- 2023Shear strengthening performance of fiber reinforced lightweight SCC beamscitations
- 2023Experimental investigation on the structural behaviour of novel non-metallic pultruded circular tubular GFRP T-joints under axial compressioncitations
- 2022Mechanical properties and chloride penetration resistances of very-low-C3A cement based SC-UHP-SFRCs incorporating metakaolin and slagcitations
- 2022Experimental Database on pullout bond performance of steel fiber embedded in ultra-high-strength concretecitations
- 2022Effects of aggregate type, aggregate pretreatment method, supplementary cementitious materials, and macro fibers on fresh and hardened properties of high-strength all-lightweight self-compacting concretecitations
- 2022Investigation on the structural failure behaviour of pultruded circular tubular GFRP multiplanar truss bridges with non-metallic connections through finite element modellingcitations
- 2021High strength flowable lightweight concrete incorporating low C3A cement, silica fume, stalite and macro-polyfelin polymer fibrescitations
- 2019Finite element simulation of circular short CFDST columns under axial compressioncitations
- 2018Experimental tests and design of rubberised concrete-filled double skin circular tubular short columnscitations
- 2018Experimental investigation of rubberised concrete-filled double skin square tubular columns under axial compressioncitations
- 2017Monitoring the on-surface synthesis of graphene nanoribbons by mass spectrometrycitations
- 2016Structural instabilities during cyclic loading of ultrafine-grained copper studied with micro bending experiments
- 2016On-surface synthesis of graphene nanoribbons with zigzag edge topologycitations
- 2014Mechanistic Study of 1,3-Butadiene Formation in Acetylene Hydrogenation over the Pd-Based Catalysts Using Density Functional Calculationscitations
- 2013Influence of surface structures, subsurface carbon and hydrogen, and surface alloying on the activity and selectivity of acetylene hydrogenation on Pd surfaces:A density functional theory studycitations
- 2013Influence of surface structures, subsurface carbon and hydrogen, and surface alloying on the activity and selectivity of acetylene hydrogenation on Pd surfaces: A density functional theory studycitations
Places of action
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article
Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction
Abstract
<jats:title>Abstract</jats:title><jats:p><jats:italic>Objective.</jats:italic> We aim to develop a Multi-modal Fusion and Feature Enhancement U-Net (MFFE U-Net) coupling with stem cell niche proximity estimation to improve voxel-wise Glioblastoma (GBM) recurrence prediction. <jats:italic>Approach.</jats:italic> 57 patients with pre- and post-surgery magnetic resonance (MR) scans were retrospectively solicited from 4 databases. Post-surgery MR scans included two months before the clinical diagnosis of recurrence and the day of the radiologicaly confirmed recurrence. The recurrences were manually annotated on the T1ce. The high-risk recurrence region was first determined. Then, a sparse multi-modal feature fusion U-Net was developed. The 50 patients from 3 databases were divided into 70% training, 10% validation, and 20% testing. 7 patients from the 4th institution were used as external testing with transfer learning. Model performance was evaluated by recall, precision, F1-score, and Hausdorff Distance at the 95% percentile (HD95). The proposed MFFE U-Net was compared to the support vector machine (SVM) model and two state-of-the-art neural networks. An ablation study was performed. <jats:italic>Main results.</jats:italic> The MFFE U-Net achieved a precision of 0.79 ± 0.08, a recall of 0.85 ± 0.11, and an F1-score of 0.82 ± 0.09. Statistically significant improvement was observed when comparing MFFE U-Net with proximity estimation couple SVM (SVM<jats:sub>PE</jats:sub>), mU-Net, and Deeplabv3. The HD95 was 2.75 ± 0.44 mm and 3.91 ± 0.83 mm for the 10 patients used in the model construction and 7 patients used for external testing, respectively. The ablation test showed that all five MR sequences contributed to the performance of the final model, with T1ce contributing the most. Convergence analysis, time efficiency analysis, and visualization of the intermediate results further discovered the characteristics of the proposed method. <jats:italic>Significance</jats:italic>. We present an advanced MFFE learning framework, MFFE U-Net, for effective voxel-wise GBM recurrence prediction. MFFE U-Net performs significantly better than the state-of-the-art networks and can potentially guide early RT intervention of the disease recurrence.</jats:p>