Name
Abstract | ||
|---|---|---|
ABSTRACTVoxel-level annotation has always been a burden of training medical image segmentation models. This paper investigates an interesting problem that finds the host organ of a lesion without actually labeling the organ. To remedy the missing annotation, we construct a graph using an off-the-shelf registration algorithm, on which lesion labels over the training set are accumulated to obtain the pseudo organ for each case. These pseudo labels are used to train a deep network, whose predictions determine the affinity of each lesion on the registration graph. We iteratively update the pseudo labels with the affinity until the training convergence. Our method is evaluated on the MSD Liver and KiTS datasets, without seeing any organ annotation, we achieve the test Dice score of 93% for liver and 92% for kidney, and boosts the accuracy of tumor segmentation to a considerable degree, $3%$, which even surpasses the model trained with ground-truth of both organ and tumor. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3503161.3548192 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zijie Yang | 1 | 0 | 1.35 |
| Lingxi Xie | 2 | 0 | 0.34 |
| Xinyue Huo | 3 | 0 | 0.34 |
| Sheng Tang | 4 | 463 | 46.27 |
| Qi Tian | 5 | 6443 | 331.75 |
| Yongdong Zhang | 6 | 2544 | 166.91 |