2019-05-24-liefers19a.md

section	title	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Contributed Papers	Dense Segmentation in Selected Dimensions: Application to Retinal Optical Coherence Tomography	We present a novel convolutional neural network architecture designed for dense segmentation in a subset of the dimensions of the input data. The architecture takes an N-dimensional image as input, and produces a label for every pixel in M output dimensions, where 0 < M < N. Large context is incorporated by an encoder-decoder structure, while funneling shortcut subnetworks provide precise localization. We demonstrate applicability of the architecture on two problems in retinal optical coherence tomography: segmentation of geographic atrophy and segmentation of retinal layers. Performance is compared against two baseline methods, that leave out either the encoder-decoder structure or the shortcut subnetworks. For segmentation of geographic atrophy, an average Dice score of 0.49 ± 0.21 was obtained, compared to 0.46 ± 0.22 and 0.28 ± 0.19 for the baseline methods, respectively. For the layer-segmentation task, the proposed architecture achieved a mean absolute error of 1.305 ± 0.547 pixels compared to 1.967 ± 0.841 and 2.166 ± 0.886± for the baseline methods.	inproceedings	Proceedings of Machine Learning Research	liefers19a	0	Dense Segmentation in Selected Dimensions: Application to Retinal Optical Coherence Tomography	337	346	337-346	337	false	Liefers, Bart and {Gonz\'alez-Gonzalo}, Cristina and Klaver, Caroline and {van Ginneken}, Bram and {S\'anchez}, Clara I.	given family Bart Liefers given family Cristina González-Gonzalo given family Caroline Klaver given prefix family Bram van Ginneken given family Clara I. Sánchez	2019-05-24		PMLR	Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning	102	inproceedings	date-parts 2019 5 24	http://proceedings.mlr.press/v102/liefers19a/liefers19a.pdf