Improving deep learning based liver vessel segmentation using automated connectivity analysis

Felix Thielke; Farina Kock; Annika Hänsch; Joachim Georgii; Nasreddin Abolmaali; Itaru Endo; Hans Meine; Andrea Schenk

doi:10.1117/12.2612526

4 April 2022 Improving deep learning based liver vessel segmentation using automated connectivity analysis

Felix Thielke, Farina Kock, Annika Hänsch, Joachim Georgii, Nasreddin Abolmaali, Itaru Endo, Hans Meine, Andrea Schenk

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120323E (2022) https://doi.org/10.1117/12.2612526
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Segmenting full vessel systems of the human liver is important for many applications in liver surgery and intervention planning. While methods exist for training DNNs for vessel segmentation, no method we know of efficiently extracts the vessel graph without modifying the DNN architecture. We demonstrate a fully automatic method for extracting and separating vessel graphs directly from the output of a segmentation model by applying a modified algorithm for vessel connectivity analysis. This method significantly improves the centerline sensitivity of reconstructed graphs on the IRCAD dataset and achieves similar scores for splitting vessel systems as the recently published TopNet approach.

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Felix Thielke, Farina Kock, Annika Hänsch, Joachim Georgii, Nasreddin Abolmaali, Itaru Endo, Hans Meine, and Andrea Schenk "Improving deep learning based liver vessel segmentation using automated connectivity analysis", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120323E (4 April 2022); https://doi.org/10.1117/12.2612526

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