000145576 001__ 145576
000145576 005__ 20200925153820.0
000145576 037__ $$aDZNE-2020-00909
000145576 041__ $$aEnglish
000145576 1001_ $$0P:(DE-2719)2812477$$aConjeti, Sailesh$$b0$$eFirst author$$udzne
000145576 1112_ $$aIEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)$$cVenice$$d2019-04-08 - 2019-04-11$$wItaly
000145576 245__ $$aDeep learning for volumetric segmentation in spatio-temporal data: Application to segmentation of prostate in DCE-MRI
000145576 260__ $$c2019
000145576 3367_ $$033$$2EndNote$$aConference Paper
000145576 3367_ $$2DataCite$$aOther
000145576 3367_ $$2BibTeX$$aINPROCEEDINGS
000145576 3367_ $$2DRIVER$$aconferenceObject
000145576 3367_ $$2ORCID$$aLECTURE_SPEECH
000145576 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1597314994_1962$$xOther
000145576 520__ $$aSegmentation of the prostate in MR images is an essential step that underpins the success of subsequent analysis methods, such as cancer lesion detection inside the tumour and registration between different modalities. This work focuses on leveraging deep learning for analysis of longitudinal volumetric datasets, particularly for the task of segmentation, and presents proof-of-concept for segmentation of the prostate in 3D+T DCE-MRI sequences. A two-stream processing pipeline is proposed for this task, comprising a spatial stream modelled using a volumetric fully convolutional network and a temporal stream modeled using recurrent neural networks with Long-Short-term Memory (LSTM) units. The predictions of the two streams are fused using deep neural networks. The proposed method has been validated on a public benchmark dataset of 17 patients, each with 40 temporal volumes. When averaged over three experiments, a highly competitive Dice overlap score of 0.8688 and sensitivity of 0.8694 were achieved. As a spatiotemporal segmentation method, it can easily migrate to other datasets.
000145576 536__ $$0G:(DE-HGF)POF3-345$$a345 - Population Studies and Genetics (POF3-345)$$cPOF3-345$$fPOF III$$x0
000145576 8564_ $$uhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8759314
000145576 909CO $$ooai:pub.dzne.de:145576$$pVDB
000145576 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2812477$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b0$$kDZNE
000145576 9131_ $$0G:(DE-HGF)POF3-345$$1G:(DE-HGF)POF3-340$$2G:(DE-HGF)POF3-300$$aDE-HGF$$bForschungsbereich Gesundheit$$lErkrankungen des Nervensystems$$vPopulation Studies and Genetics$$x0
000145576 9141_ $$y2019
000145576 9201_ $$0I:(DE-2719)1040310$$kAG Reuter$$lImage Analysis$$x0
000145576 980__ $$aconf
000145576 980__ $$aVDB
000145576 980__ $$aI:(DE-2719)1040310
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