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000179418 1001_ $$0P:(DE-He78)9d0e93f03c73f265ef93b2217b023d60$$aSchellenberg, Melanie$$b0$$eFirst author$$udkfz
000179418 245__ $$aSemantic segmentation of multispectral photoacoustic images using deep learning.
000179418 260__ $$aAmsterdam ˜[u.a.]œ$$bElsevier$$c2022
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000179418 520__ $$aPhotoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic and ultrasound imaging data are used as reference and enable the training of a deep learning-based segmentation algorithm in a supervised manner. Based on a validation study with experimentally acquired data from 16 healthy human volunteers, we show that automatic tissue segmentation can be used to create powerful analyses and visualizations of multispectral photoacoustic images. Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.
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000179418 650_7 $$2Other$$aDeep learning
000179418 650_7 $$2Other$$aMedical image segmentation
000179418 650_7 $$2Other$$aMultispectral imaging
000179418 650_7 $$2Other$$aOptoacoustics
000179418 650_7 $$2Other$$aPhotoacoustics
000179418 7001_ $$0P:(DE-He78)84acbc6406dd178828f87a8150d40951$$aDreher, Kris K$$b1$$udkfz
000179418 7001_ $$0P:(DE-He78)1c47bf7bdef42ec57b194723ccfb2946$$aHolzwarth, Niklas$$b2$$udkfz
000179418 7001_ $$0P:(DE-He78)7ea9af59d03ec7deb982a0e0562358fa$$aIsensee, Fabian$$b3$$udkfz
000179418 7001_ $$0P:(DE-He78)97e904f47dab556a77c0149cd0002591$$aReinke, Annika$$b4$$udkfz
000179418 7001_ $$0P:(DE-He78)0d054b6843ace36d1c965b6cb938d1c9$$aSchreck, Nicholas$$b5$$udkfz
000179418 7001_ $$0P:(DE-He78)a83df473f58a6a8ef43263ec9783ecf0$$aSeitel, Alexander$$b6$$udkfz
000179418 7001_ $$0P:(DE-He78)26651d9aa10255ad4f35610a56aa91e8$$aTizabi, Minu D$$b7$$udkfz
000179418 7001_ $$0P:(DE-He78)26a1176cd8450660333a012075050072$$aMaier-Hein, Lena$$b8$$udkfz
000179418 7001_ $$0P:(DE-He78)fd657bfbb3c4757ac029bb6b56ab9b71$$aGröhl, Janek$$b9$$eLast author
000179418 773__ $$0PERI:(DE-600)2716706-9$$a10.1016/j.pacs.2022.100341$$gVol. 26, p. 100341 -$$p100341$$tPhotoacoustics$$v26$$x2213-5979$$y2022
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