ArXiv Preprint
Peripancreatic vessel segmentation and anatomical labeling play extremely
important roles to assist the early diagnosis, surgery planning and prognosis
for patients with pancreatic tumors. However, most current techniques cannot
achieve satisfactory segmentation performance for peripancreatic veins and
usually make predictions with poor integrity and connectivity. Besides,
unsupervised labeling algorithms cannot deal with complex anatomical variation
while fully supervised methods require a large number of voxel-wise annotations
for training, which is very labor-intensive and time-consuming. To address
these problems, we propose our Automated Peripancreatic vEssel Segmentation and
lAbeling (APESA) framework, to not only highly improve the segmentation
performance for peripancreatic veins, but also efficiently identify the
peripancreatic artery branches. There are two core modules in our proposed
APESA framework: iterative trunk growth module (ITGM) for vein segmentation and
weakly supervised labeling mechanism (WSLM) for artery branch identification.
Our proposed ITGM is composed of a series of trunk growth modules, each of
which chooses the most reliable trunk of a basic vessel prediction by the
largest connected constraint, and seeks for the possible growth branches by
branch proposal network. Our designed iterative process guides the raw trunk to
be more complete and fully connected. Our proposed WSLM consists of an
unsupervised rule-based preprocessing for generating pseudo branch annotations,
and an anatomical labeling network to learn the branch distribution voxel by
voxel. We achieve Dice of 94.01% for vein segmentation on our collected
dataset, which boosts the accuracy by nearly 10% compared with the
state-of-the-art methods. Additionally, we also achieve Dice of 97.01% on
segmentation and competitive performance on anatomical labeling for
peripancreatic arteries.
Liwen Zou, Zhenghua Cai, Liang Mao, Ziwei Nie, Yudong Qiu, Xiaoping Yang
2023-03-06
