FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328107813> ?p ?o ?g. }

• W4328107813 endingPage "100431" @default.
• W4328107813 startingPage "100431" @default.
• W4328107813 abstract "The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation in computed tomography (CT) is challenging. This work sought to: i) propose an automatic pipeline for intraprostatic urethra segmentation in CT, ii) analyze the dose to the urethra, iii) compare the predictions to magnetic resonance (MR) contours.First, we trained Deep Learning networks to segment the rectum, bladder, prostate, and seminal vesicles. Then, the proposed Deep Learning Urethra Segmentation model was trained with the bladder and prostate distance transforms and 44 labeled CT with visible catheters. The evaluation was performed on 11 datasets, calculating centerline distance (CLD) and percentage of centerline within 3.5 and 5 mm. We applied this method to a dataset of 32 patients treated with intensity-modulated radiation therapy (IMRT) to quantify the urethral dose. Finally, we compared predicted intraprostatic urethra contours to manual delineations in MR for 15 patients without catheter.A mean CLD of 1.6 ± 0.8 mm for the whole urethra and 1.7 ± 1.4, 1.5 ± 0.9, and 1.7 ± 0.9 mm for the top, middle, and bottom thirds were obtained in CT. On average, 94% and 97% of the segmented centerlines were within a 3.5 mm and 5 mm radius, respectively. In IMRT, the urethra received a higher dose than the overall prostate. We also found a slight deviation between the predicted and manual MR delineations.A fully-automatic segmentation pipeline was validated to delineate the intraprostatic urethra in CT images." @default.
• W4328107813 created "2023-03-22" @default.
• W4328107813 creator A5012333862 @default.
• W4328107813 creator A5012371603 @default.
• W4328107813 creator A5018063898 @default.
• W4328107813 creator A5027115231 @default.
• W4328107813 creator A5027631109 @default.
• W4328107813 creator A5039254152 @default.
• W4328107813 creator A5039902611 @default.
• W4328107813 creator A5045871192 @default.
• W4328107813 creator A5057280769 @default.
• W4328107813 creator A5075187500 @default.
• W4328107813 creator A5077224068 @default.
• W4328107813 date "2023-04-01" @default.
• W4328107813 modified "2023-10-15" @default.
• W4328107813 title "Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning" @default.
• W4328107813 cites W1979387793 @default.
• W4328107813 cites W1987869189 @default.
• W4328107813 cites W1997716779 @default.
• W4328107813 cites W2043184052 @default.
• W4328107813 cites W2092523554 @default.
• W4328107813 cites W2153847096 @default.
• W4328107813 cites W2208191807 @default.
• W4328107813 cites W2401803281 @default.
• W4328107813 cites W2762517245 @default.
• W4328107813 cites W2790089720 @default.
• W4328107813 cites W2805205462 @default.
• W4328107813 cites W2889147523 @default.
• W4328107813 cites W2912734771 @default.
• W4328107813 cites W2923425487 @default.
• W4328107813 cites W2946147212 @default.
• W4328107813 cites W2949153442 @default.
• W4328107813 cites W2996024615 @default.
• W4328107813 cites W3004802547 @default.
• W4328107813 cites W3012673439 @default.
• W4328107813 cites W3013902489 @default.
• W4328107813 cites W3045464562 @default.
• W4328107813 cites W3112701542 @default.
• W4328107813 cites W3149185561 @default.
• W4328107813 cites W3161611093 @default.
• W4328107813 cites W3162643763 @default.
• W4328107813 cites W3209108213 @default.
• W4328107813 cites W4206841660 @default.
• W4328107813 cites W4212928906 @default.
• W4328107813 cites W4221008799 @default.
• W4328107813 cites W4223488687 @default.
• W4328107813 cites W3165807743 @default.
• W4328107813 doi "https://doi.org/10.1016/j.phro.2023.100431" @default.
• W4328107813 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37007914" @default.
• W4328107813 hasPublicationYear "2023" @default.
• W4328107813 type Work @default.
• W4328107813 citedByCount "1" @default.
• W4328107813 countsByYear W43281078132023 @default.
• W4328107813 crossrefType "journal-article" @default.
• W4328107813 hasAuthorship W4328107813A5012333862 @default.
• W4328107813 hasAuthorship W4328107813A5012371603 @default.
• W4328107813 hasAuthorship W4328107813A5018063898 @default.
• W4328107813 hasAuthorship W4328107813A5027115231 @default.
• W4328107813 hasAuthorship W4328107813A5027631109 @default.
• W4328107813 hasAuthorship W4328107813A5039254152 @default.
• W4328107813 hasAuthorship W4328107813A5039902611 @default.
• W4328107813 hasAuthorship W4328107813A5045871192 @default.
• W4328107813 hasAuthorship W4328107813A5057280769 @default.
• W4328107813 hasAuthorship W4328107813A5075187500 @default.
• W4328107813 hasAuthorship W4328107813A5077224068 @default.
• W4328107813 hasBestOaLocation W43281078131 @default.
• W4328107813 hasConcept C121608353 @default.
• W4328107813 hasConcept C126322002 @default.
• W4328107813 hasConcept C126838900 @default.
• W4328107813 hasConcept C126894567 @default.
• W4328107813 hasConcept C143409427 @default.
• W4328107813 hasConcept C154945302 @default.
• W4328107813 hasConcept C201645570 @default.
• W4328107813 hasConcept C2776235491 @default.
• W4328107813 hasConcept C2777085111 @default.
• W4328107813 hasConcept C2777957205 @default.
• W4328107813 hasConcept C2780192828 @default.
• W4328107813 hasConcept C2989005 @default.
• W4328107813 hasConcept C41008148 @default.
• W4328107813 hasConcept C509974204 @default.
• W4328107813 hasConcept C71924100 @default.
• W4328107813 hasConcept C89600930 @default.
• W4328107813 hasConceptScore W4328107813C121608353 @default.
• W4328107813 hasConceptScore W4328107813C126322002 @default.
• W4328107813 hasConceptScore W4328107813C126838900 @default.
• W4328107813 hasConceptScore W4328107813C126894567 @default.
• W4328107813 hasConceptScore W4328107813C143409427 @default.
• W4328107813 hasConceptScore W4328107813C154945302 @default.
• W4328107813 hasConceptScore W4328107813C201645570 @default.
• W4328107813 hasConceptScore W4328107813C2776235491 @default.
• W4328107813 hasConceptScore W4328107813C2777085111 @default.
• W4328107813 hasConceptScore W4328107813C2777957205 @default.
• W4328107813 hasConceptScore W4328107813C2780192828 @default.
• W4328107813 hasConceptScore W4328107813C2989005 @default.
• W4328107813 hasConceptScore W4328107813C41008148 @default.
• W4328107813 hasConceptScore W4328107813C509974204 @default.
• W4328107813 hasConceptScore W4328107813C71924100 @default.
• W4328107813 hasConceptScore W4328107813C89600930 @default.

• next