Name
Title | ||
|---|---|---|
Patch-Free 3D Medical Image Segmentation Driven by Super-Resolution Technique and Self-Supervised Guidance |
Abstract | ||
|---|---|---|
3D medical image segmentation with high resolution is an important issue for accurate diagnosis. The main challenge for this task is its large computational cost and GPU memory restriction. Most of the existing 3D medical image segmentation methods are patch-based methods, which ignore the global context information for accurate segmentation and also reduce the efficiency of inference. To tackle this problem, we propose a patch-free 3D medical image segmentation method, which can realize high-resolution (HR) segmentation with low-resolution (LR) input. It contains a multi-task learning framework (Semantic Segmentation and Super-Resolution (SR)) and a Self-Supervised Guidance Module (SGM). SR is used as an auxiliary task for the main segmentation task to restore the HR details, while the SGM, which uses the original HR image patch as a guidance image, is designed to keep the high-frequency information for accurate segmentation. Besides, we also introduce a Task-Fusion Module (TFM) to exploit the inter connections between the segmentation and SR tasks. Since the SR task and TFM are only used in the training phase, they do not introduce extra computational costs when predicting. We conduct the experiments on two different datasets, and the experimental results show that our framework outperforms current patch-based methods as well as has a 4x higher speed when predicting. Our codes are available at https://github.com/ Dootmaan/PFSeg. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87193-2_13 | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT I |
Keywords | DocType | Volume |
3D medical image segmentation, Patch-free, Multi-task learning | Conference | 12901 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyi Wang | 1 | 0 | 0.68 |
| Lanfen Lin | 2 | 4 | 8.67 |
| Hongjie Hu | 3 | 11 | 9.50 |
| Qingqing Chen | 4 | 6 | 3.86 |
| Yinhao Li | 5 | 0 | 0.34 |
| Yutaro Iwamoto | 6 | 13 | 17.95 |
| Xian-Hua Han | 7 | 14 | 10.19 |
| Yen-Wei Chen | 8 | 720 | 155.73 |
| Ruofeng Tong | 9 | 466 | 49.69 |