  CNNs have been widely applied for medical image analysis. However, limited
memory capacity is one of the most common drawbacks of processing
high-resolution 3D volumetric data. 3D volumes are usually cropped or downsized
first before processing, which can result in a loss of resolution, increase
class imbalance, and affect the performance of the segmentation algorithms. In
this paper, we propose an end-to-end deep learning approach called IP-UNet.
IP-UNet is a UNet-based model that performs multi-class segmentation on
Intensity Projection (IP) of 3D volumetric data instead of the memory-consuming
3D volumes. IP-UNet uses limited memory capability for training without losing
the original 3D image resolution. We compare the performance of three models in
terms of segmentation accuracy and computational cost: 1) Slice-by-slice 2D
segmentation of the CT scan images using a conventional 2D UNet model. 2)
IP-UNet that operates on data obtained by merging the extracted Maximum
Intensity Projection (MIP), Closest Vessel Projection (CVP), and Average
Intensity Projection (AvgIP) representations of the source 3D volumes, then
applying the UNet model on the output IP images. 3) 3D-UNet model directly
reads the 3D volumes constructed from a series of CT scan images and outputs
the 3D volume of the predicted segmentation. We test the performance of these
methods on 3D volumetric images for automatic breast calcification detection.
Experimental results show that IP-Unet can achieve similar segmentation
accuracy with 3D-Unet but with much better performance. It reduces the training
time by 70\% and memory consumption by 92\%.
