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| Home > Publications Database > Deep learning for volumetric segmentation in spatio-temporal data: Application to segmentation of prostate in DCE-MRI > print |
| 001 | 145576 | ||
| 005 | 20200925153820.0 | ||
| 037 | _ | _ | |a DZNE-2020-00909 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Conjeti, Sailesh |0 P:(DE-2719)2812477 |b 0 |e First author |u dzne |
| 111 | 2 | _ | |a IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) |c Venice |d 2019-04-08 - 2019-04-11 |w Italy |
| 245 | _ | _ | |a Deep learning for volumetric segmentation in spatio-temporal data: Application to segmentation of prostate in DCE-MRI |
| 260 | _ | _ | |c 2019 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1597314994_1962 |2 PUB:(DE-HGF) |x Other |
| 520 | _ | _ | |a Segmentation 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. |
| 536 | _ | _ | |a 345 - Population Studies and Genetics (POF3-345) |0 G:(DE-HGF)POF3-345 |c POF3-345 |f POF III |x 0 |
| 856 | 4 | _ | |u https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8759314 |
| 909 | C | O | |o oai:pub.dzne.de:145576 |p VDB |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 0 |6 P:(DE-2719)2812477 |
| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Gesundheit |l Erkrankungen des Nervensystems |1 G:(DE-HGF)POF3-340 |0 G:(DE-HGF)POF3-345 |2 G:(DE-HGF)POF3-300 |v Population Studies and Genetics |x 0 |
| 914 | 1 | _ | |y 2019 |
| 920 | 1 | _ | |0 I:(DE-2719)1040310 |k AG Reuter |l Image Analysis |x 0 |
| 980 | _ | _ | |a conf |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-2719)1040310 |
| 980 | _ | _ | |a UNRESTRICTED |
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