Automatic segmentation of stroke lesions in non-contrast computed - - PowerPoint PPT Presentation

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Aug 22, 2022 476 likes •551 views

Automatic segmentation of stroke lesions in non-contrast computed tomography with convolutional neural networks Anup Serena Deepthi Helge C. Jens Nils D. Tuladhar 1 * Schimert 1* Rajashekar 1 Kniep 2 Fiehler 2 Forkert 1 Full Paper Trained

SLIDE 1

Automatic segmentation of stroke lesions in non-contrast computed tomography with convolutional neural networks

Anup Tuladhar 1* Serena Schimert 1* Deepthi Rajashekar 1 Helge C. Kniep 2 Jens Fiehler 2 Nils D. Forkert 1

*

1

University Medical Center Hamburg-Eppendorf

2

doi: 10.1109/ACCESS.2020.2995632 Full Paper doi: 10.21227/jps9-0b57 Trained Model

Co-first authors

SLIDE 2

CNN for segmenting Non-contrast Computed Tomography (NCCT) datasets

[1] Demchuk et al, Int. J. Stroke (2015) [2] Fiehler et al, Stroke (2019) [3] Kamnitsas et al, Medical Image Analysis (2017) [4] DeepMedic: https://github.com/deepmedic/deepmedic

291 multi-center datasets of follow-up NCCT stroke imaging from ESCAPE1 and ERASER2 trials

Validation

48 datasets

Training

204 datasets

ESCAPE1

22 centers

ERASER2

7 centers

Test

39 datasets

Out-of-distribution Multiple raters Trained CNN based on DeepMedic3 framework4

3D CNN kernels
3 parallel convolutional pathways for

multi-scale processing (1×, 3× and 5×)

Trained model available:

doi: 10.21227/jps9-0b57

SLIDE 3

Improving CNN segmentations with post-processing

Tuladhar et al., IEEE Access (2020), doi: 10.1109/ACCESS.2020.2995632

Used post-processing to improve segmentation accuracy

Connected-component analysis

to exclude small lesions fragments < 1.5 mL

Hole-filling kernel of 3 voxels
Used validation dataset to
ptimize parameters

SLIDE 4

Post-processing improves segmentations of trained CNN

: P < 0.05 : P < 0.01