The task of blood vessel segmentation in microscopy images is crucial for many diagnostic and research applications. However, vessels can look vastly different, depending on the transient imaging conditions, and collecting data for supervised training is laborious. We present a novel deep learning method for unsupervised segmentation of blood vessels. The method is inspired by the field of active contours and we introduce a new loss term, which is based on the morphological Active Contours Without Edges (ACWE) optimization method. The role of the morphological operators is played by novel pooling layers that are incorporated to the network's architecture. We demonstrate the challenges that are faced by previous supervised learning solutions, when the imaging conditions shift. Our unsupervised method is able to outperform such previous methods in both the labeled dataset, and when applied to similar but different datasets. Our code, as well as efficient pytorch reimplementations of the baseline methods VesselNN and DeepVess are attached as supplementary.
|Original language||American English|
|Title of host publication||Proceedings - 2019 International Conference on Computer Vision, ICCV 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - Oct 2019|
|Event||17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of|
Duration: 27 Oct 2019 → 2 Nov 2019
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Conference||17th IEEE/CVF International Conference on Computer Vision, ICCV 2019|
|Country/Territory||Korea, Republic of|
|Period||27/10/19 → 2/11/19|
Bibliographical noteFunding Information:
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC grants CoG 725974 and StG 639416). The contribution of the first author is part of a Ph.D. thesis research conducted at Tel Aviv University. The authors thank David Kain for conducting the mouse surgery and Yulia Mitiagin for segmenting the said datasets using the VIDA suite .
© 2019 IEEE.