  Segmentation of multiple organs-at-risk (OARs) is essential for radiation
therapy treatment planning and other clinical applications. We developed an
Automated deep Learning-based Abdominal Multi-Organ segmentation (ALAMO)
framework based on 2D U-net and a densely connected network structure with
tailored design in data augmentation and training procedures such as deep
connection, auxiliary supervision, and multi-view. The model takes in
multi-slice MR images and generates the output of segmentation results.
Three-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of
102 subjects were collected and used in our study. Ten OARs were studied,
including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum,
small intestine, spinal cord, and vertebral bodies. Two radiologists manually
labeled and obtained the consensus contours as the ground-truth. In the
complete cohort of 102, 20 samples were held out for independent testing, and
the rest were used for training and validation. The performance was measured
using volume overlapping and surface distance. The ALAMO framework generated
segmentation labels in good agreement with the manual results. Specifically,
among the 10 OARs, 9 achieved high Dice Similarity Coefficients (DSCs) in the
range of 0.87-0.96, except for the duodenum with a DSC of 0.80. The inference
completes within one minute for a 3D volume of 320x288x180. Overall, the ALAMO
model matches the state-of-the-art performance. The proposed ALAMO framework
allows for fully automated abdominal MR segmentation with high accuracy and low
memory and computation time demands.
