Efficient Connectivity-Preserving Instance Segmentation with Supervoxel-Based Loss Function
Authors: Anna Grim, Jayaram Chandrashekar, Uygar Sümbül
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on the following image segmentation datasets: DRIVE, ISBI12, Crack Tree and EXASPIM3. ... Table 1 shows quantitative results for the different models on the segmentation datasets. Our proposed method achieves state-of-the-art results; particularly, for the topologically relevant metrics. |
| Researcher Affiliation | Industry | Anna Grim, Jayaram Chandrashekar, Uygar S umb ul Allen Institute 615 Westlake Avenue Seattle, WA 98109 USA EMAIL |
| Pseudocode | Yes | Note that pseudocode for this method is provided in Algo. 1 and 2 |
| Open Source Code | Yes | Note that pseudocode for this method is provided in Algo. 1 and 2 and our code is publicly available at https://github.com/Allen Neural Dynamics/supervoxel-loss. |
| Open Datasets | Yes | We evaluate our method on the following image segmentation datasets: DRIVE, ISBI12, Crack Tree and EXASPIM3. ... DRIVE is a retinal vessel dataset consisting of 20 images with dimensions 584x565 (Staal et al. 2004). ... EXASPIM is a 3-d light sheet microscopy dataset consisting of 37 images whose dimensions range from 256x256x256 to 1024x1024x1024 and voxel size is 1 µm3 (Glaser et al. 2024). Download at s3://aind-msma-morphology-data/EXASPIM25 |
| Dataset Splits | Yes | For the 2-d datasets, we perform 3-fold cross-validation for each method and report the mean and standard deviation across the validation set. For the 3-d dataset, we evaluate the methods on a test set consisting of 4 images. |
| Hardware Specification | No | No specific hardware details (GPU models, CPU types, memory) are mentioned in the paper. It only discusses runtime, e.g., 'Runtime/Epoch' in Table 2, but not the hardware used to achieve it. |
| Software Dependencies | No | The paper does not explicitly list any software dependencies with specific version numbers. It mentions deep learning frameworks in general, but no concrete details. |
| Experiment Setup | Yes | In all experiments, we set α = 0.5 and β = 0.5 in our proposed topological loss function. ... We recommend training a baseline model with a standard loss function, then fine-tuning with the topological loss function. ... Our experiments show that α 0.5 and β 0.5 are optimal for ISBI12, whereas α 0.9 and β 0.8 are optimal for EXASPIM (Figure 7). |