22 research outputs found
Towards image-guided pancreas and biliary endoscopy: Automatic multi-organ segmentation on abdominal CT with dense dilated networks
Segmentation of anatomy on abdominal CT enables patient-specific image guidance in clinical endoscopic procedures and in endoscopy training. Because robust interpatient registration of abdominal images is necessary for existing multi-atlas- and statistical-shape-model-based segmentations, but remains challenging, there is a need for automated multi-organ segmentation that does not rely on registration. We present a deep-learning-based algorithm for segmenting the liver, pancreas, stomach, and esophagus using dilated convolution units with dense skip connections and a new spatial prior. The algorithm was evaluated with an 8-fold cross-validation and compared to a joint-label-fusion-based segmentation based on Dice scores and boundary distances. The proposed algorithm yielded more accurate segmentations than the joint-label-fusion-ba sed algorithm for the pancreas (median Dice scores 66 vs 37), stomach (83 vs 72) and esophagus (73 vs 54) and marginally less accurate segmentation for the liver (92 vs 93). We conclude that dilated convolutional networks with dense skip connections can segment the liver, pancreas, stomach and esophagus from abdominal CT without image registration and have the potential to support image-guided navigation in gastrointestinal endoscopy procedures
A New Probabilistic V-Net Model with Hierarchical Spatial Feature Transform for Efficient Abdominal Multi-Organ Segmentation
Accurate and robust abdominal multi-organ segmentation from CT imaging of
different modalities is a challenging task due to complex inter- and
intra-organ shape and appearance variations among abdominal organs. In this
paper, we propose a probabilistic multi-organ segmentation network with
hierarchical spatial-wise feature modulation to capture flexible organ semantic
variants and inject the learnt variants into different scales of feature maps
for guiding segmentation. More specifically, we design an input decomposition
module via a conditional variational auto-encoder to learn organ-specific
distributions on the low dimensional latent space and model richer organ
semantic variations that is conditioned on input images.Then by integrating
these learned variations into the V-Net decoder hierarchically via spatial
feature transformation, which has the ability to convert the variations into
conditional Affine transformation parameters for spatial-wise feature maps
modulating and guiding the fine-scale segmentation. The proposed method is
trained on the publicly available AbdomenCT-1K dataset and evaluated on two
other open datasets, i.e., 100 challenging/pathological testing patient cases
from AbdomenCT-1K fully-supervised abdominal organ segmentation benchmark and
90 cases from TCIA+&BTCV dataset. Highly competitive or superior quantitative
segmentation results have been achieved using these datasets for four abdominal
organs of liver, kidney, spleen and pancreas with reported Dice scores improved
by 7.3% for kidneys and 9.7% for pancreas, while being ~7 times faster than two
strong baseline segmentation methods(nnUNet and CoTr).Comment: 12 pages, 6 figure