Mism-233 Jun 2026

| Dataset | Method | DSC ↑ | HD95 ↓ (mm) | ASD ↓ (mm) | Time ↓ (s) | |---------|--------|------|------------|------------|-----------| | BraTS‑2021 | nnU‑Net | 88.8 ± 1.1 | 5.6 ± 0.9 | 1.2 ± 0.3 | 0.19 | | | Swin‑UNETR | 89.4 ± 0.9 | 5.1 ± 0.8 | 1.1 ± 0.2 | 0.22 | | | Wavelet‑CNN | 87.9 ± 1.3 | 6.3 ± 1.0 | 1.4 ± 0.4 | 0.15 | | | (ours) | 91.2 ± 0.8 | 3.9 ± 0.6 | 0.8 ± 0.2 | 0.12 | | KiTS‑19 | nnU‑Net | 82.3 ± 1.4 | 7.1 ± 1.2 | 1.8 ± 0.5 | 0.21 | | | Swin‑UNETR | 83.0 ± 1.2 | 6.9 ± 1.1 | 1.7 ± 0.4 | 0.24 | | | Morphology‑Net | 81.5 ± 1.5 | 7.4 ± 1.3 | 1.9 ± 0.6 | 0.14 | | | MISM‑233 | 84.7 ± 1.1 | 5.0 ± 0.8 | 1.3 ± 0.3 | 0.13 | | MSD‑Liver | nnU‑Net | 87.4 ± 1.0 | 4.8

| Dataset | Modality | # Cases | ROI | Pre‑processing | |---------|----------|--------|-----|----------------| | BraTS‑2021 | Multi‑modal MRI (T1‑c, T2‑FLAIR, T1, T2) | 1251 | Glioma (whole tumor) | N4 bias correction, Z‑score | | KiTS‑19 | Contrast‑enhanced CT | 210 | Kidney & tumor | Resample to 1 mm³, clip HU (−200, 300) | | MSD‑Liver | CT (portal venous phase) | 131 | Liver | Same as KiTS‑19 | mism-233

Accurate 3‑D segmentation of anatomical structures remains a bottleneck for quantitative imaging, especially when lesions exhibit heterogeneous texture and variable size. Existing deep‑learning models either sacrifice fine‑scale detail for context or struggle with spectral (intensity‑frequency) variability across scanners. Methods. We propose MISM‑233 , a Multi‑Scale Integrated Spectral‑Morphology framework that couples (i) a Spectral Attention Encoder (SAE) extracting frequency‑domain features via learned wavelet‑type filters, (ii) a Morphology‑Guided Decoder (MGD) that injects multi‑scale shape priors using learned morphological operators, and (iii) a Cross‑Scale Fusion (CSF) module that iteratively refines voxel‑wise predictions through a gated attention mechanism. The network is trained end‑to‑end with a compound loss comprising Dice, boundary‑aware Hausdorff, and a novel Spectral Consistency term. Results. On three public 3‑D datasets (BraTS‑2021, KiTS‑19, and MSD‑Liver), MISM‑233 achieves average Dice scores of 91.2 % (±0.8) , 84.7 % (±1.1) , and 89.5 % (±0.9) respectively—improving over the current state‑of‑the‑art (nnU‑Net, TransUNet, and Swin‑UNETR) by +2.4 % , +3.1 % , and +2.0 % Dice. The model reduces the 95 % Hausdorff distance by ≈ 30 % and runs at 0.12 s/volume on a single RTX 3090. Conclusion. By jointly leveraging spectral cues and morphology‑aware shape priors across multiple scales, MISM‑233 delivers robust, high‑resolution segmentations that generalize across imaging modalities and scanner protocols. The proposed framework is openly released (GitHub link) and can be adapted to other volumetric tasks. | Dataset | Method | DSC ↑ |

: Tailoring internal reporting tools to meet specific departmental needs. On three public 3‑D datasets (BraTS‑2021, KiTS‑19, and

There is a consistent demand for professionals who can speak both "business" and "code." Research suggests that while MIS is often viewed as less demanding than pure computer science, it is considered more challenging than traditional liberal arts or non-quantitative business degrees due to this dual requirement. Mastering web-based programming ensures that MIS graduates are not just "information consumers" but "information architects" capable of prototyping solutions that solve real-world business problems. Conclusion