Fully-automated functional region annotation of liver via a 2.5D class-aware deep neural network with spatial adaptation.

• To our best knowledge, we are the first to use deep learning for fully automatic functional region annotation of liver, demonstrating its ability to meet the challenge of classifying hepatic segments. • The anatomy class-specific information was used to solve the problem of the class crossing between slices. • A 2.5D spatial adaptation input module to overcome the problem of inconsistency in slices spatial information was designed. • We manually annotated 112 CT cases from the Task08_HepaticVessel in the Medical segmentation Decathlon study [25] over 3 months, and validated this pipeline on our data. Automatic functional region annotation of liver should be very useful for preoperative planning of liver resection in the clinical domain. However, many traditional computer-aided annotation methods based on anatomical landmarks or the vascular tree often fail to extract accurate liver segments. Furthermore, these methods are difficult to fully automate and thus remain time-consuming. To address these issues, in this study we aim to develop a fully-automated approach for functional region annotation of liver using deep learning based on 2.5D class-aware deep neural networks with spatial adaptation. 112 CT scans were fed into our 2.5D class-aware deep neural network with spatial adaptation for automatic functional region annotation of liver. The proposed model was built upon the ResU-net architecture, which adaptively selected a stack of adjacent CT slices as input and, generating masks corresponding to the center slice, automatically annotated the liver functional region from abdominal CT images. Furthermore, to minimize the problem of class-level ambiguity between different slices, the anatomy class-specific information was used. The final algorithm performance for automatic functional region annotation of liver showed large overlap with that of manual reference segmentation. The dice similarity coefficient of hepatic segments achieved high scores and an average dice score of 0.882. The entire calculation time was quite fast (~5 s) compared to manual annotation (~2.5 hours). The proposed models described in this paper offer a feasible solution for fully-automated functional region annotation of liver from CT images. The experimental results demonstrated that the proposed method can attain a high average dice score and low computational time. Therefore, this work should allow for improved liver surgical resection planning by our precise segmentation and simple fully-automated method. [ABSTRACT FROM AUTHOR]