AdaptNet: Adaptive Learning from Partially Labeled Data for Abdomen Multi-organ and Tumor Segmentation

Due to the high costs associated with the labor and expertise required for annotating 3D medical images at the voxel level, most public and in-house datasets only include annotations of a single (or a few) organ or tumor. This limitation results in what is commonly referred to as the 'partial labeling/annotation problem'. In order to tackle this issue, we introduce an adaptive learning network, AdaptNet, to effectively segment multiple organs and tumors within partially labeled data from abdomen CT images. AdaptNet comprises three key components: a segmentation network, a pseudo-label generation network, and an adaptive controller responsible for generating dynamic weights. AdaptNet generates adaptive weights dynamically through the controller, which takes into account the balance of the partial labels and the corresponding pseudo-labels. This approach enables AdaptNet to efficiently and flexibly learn multiple organ and tumor information from the partial labeling/annotation dataset, which is typically performed by multiple or multi-head networks. We conduct validation on a large-scale partially annotated dataset under MICCAI FLARE 2023 challenge and demonstrate that the proposed AdaptNet outperforms the baseline method across the 13 different organ and tumor segmentation tasks. Our method achieves a mean organ Dice Similarity Coefficient (DSC) of 89.61% and a Normalized Surface Dice (NSD) of 94.94%, and a tumor DSC and NSD of 39.16% and 30.52% on the FLARE 2023 online validation. Additionally, in the Final Testing dataset, our method achieves a mean organ DSC and NSD of 89.34% and 95.26% and a tumor DSC and NSD of 54.59% and 40.78%, and the area under GPU memory-time curve is 33.35 s and 84276 MB. The code is available at https://github.com/Prech-start/FLARE23_AdaptNet.