# IP-UNet: Intensity Projection UNet Architecture for 3D Medical Volume Segmentation

arXiv: https://arxiv.org/abs/2308.12761 · HTML: https://mlsanity.com/paper/2308.12761/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection](https://mlsanity.com/paper/1904.05956/) (2019)
- [A two-stage 3D Unet framework for multi-class segmentation on full resolution image](https://mlsanity.com/paper/1804.04341/) (2018)
- [Tracing in 2D to Reduce the Annotation Effort for 3D Deep Delineation](https://mlsanity.com/paper/1811.10508/) (2018)
- [Addressing the Memory Bottleneck in AI Model Training](https://mlsanity.com/paper/2003.08732/) (2020)

## Built on by (most cited first, 2 of 2)

- [Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems](https://mlsanity.com/paper/2402.16904/) (2024)
- [Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations](https://mlsanity.com/paper/2402.16052/) (2024)

## Abstract (arXiv)

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%.