  In this work, we present a memory-efficient fully convolutional network (FCN)
incorporated with several memory-optimized techniques to reduce the run-time
GPU memory demand during training phase. In medical image segmentation tasks,
subvolume cropping has become a common preprocessing. Subvolumes (or small
patch volumes) were cropped to reduce GPU memory demand. However, small patch
volumes capture less spatial context that leads to lower accuracy. As a pilot
study, the purpose of this work is to propose a memory-efficient FCN which
enables us to train the model on full size CT image directly without subvolume
cropping, while maintaining the segmentation accuracy. We optimize our network
from both architecture and implementation. With the development of computing
hardware, such as graphics processing unit (GPU) and tensor processing unit
(TPU), now deep learning applications is able to train networks with large
datasets within acceptable time. Among these applications, semantic
segmentation using fully convolutional network (FCN) also has gained a
significant improvement against traditional image processing approaches in both
computer vision and medical image processing fields. However, unlike general
color images used in computer vision tasks, medical images have larger scales
than color images such as 3D computed tomography (CT) images, micro CT images,
and histopathological images. For training these medical images, the large
demand of computing resource become a severe problem. In this paper, we present
a memory-efficient FCN to tackle the high GPU memory demand challenge in organ
segmentation problem from clinical CT images. The experimental results
demonstrated that our GPU memory demand is about 40% of baseline architecture,
parameter amount is about 30% of the baseline.
