  The precise spatial and quantitative delineation of indistinct-boundary
medical objects is paramount for the accuracy of diagnostic protocols, efficacy
of surgical interventions, and reliability of postoperative assessments.
Despite their significance, the effective segmentation and instantaneous
three-dimensional reconstruction are significantly impeded by the paucity of
representative samples in available datasets and noise artifacts. To surmount
these challenges, we introduced Stochastic Defect Injection (SDi) to augment
the representational diversity of challenging indistinct-boundary objects
within training corpora. Consequently, we propose the Dual-Encoder Fourier
Group Harmonics Network (DEFN) to tailor noise filtration, amplify detailed
feature recognition, and bolster representation across diverse medical imaging
scenarios. By incorporating Dynamic Weight Composing (DWC) loss dynamically
adjusts model's focus based on training progression, DEFN achieves SOTA
performance on the OIMHS public dataset, showcasing effectiveness in indistinct
boundary contexts. Source code for DEFN is available at:
https://github.com/IMOP-lab/DEFN-pytorch.
