Topology-Preserving Deep Image Segmentation
Authors: Xiaoling Hu, Fuxin Li, Dimitris Samaras, Chao Chen
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is empirically validated by comparing with state-of-the-arts on natural and biomedical datasets with fine-scale structures. It achieves superior performance on metrics that encourage structural accuracy. In particular, our method significantly outperforms others on the Betti number error which exactly measures the topological accuracy. |
| Researcher Affiliation | Academia | Xiaoling Hu1, Li Fuxin2, Dimitris Samaras1 and Chao Chen1 1Stony Brook University 2Oregon State University |
| Pseudocode | No | The paper does not include structured pseudocode or algorithm blocks. It describes the computational steps and gradients in paragraph text. |
| Open Source Code | No | The paper does not contain any explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We evaluate our method on six natural and biomedical datasets: CREMI7, ISBI12 [4], ISBI13 [3], Crack Tree [48], Road [28] and DRIVE [39]. The citations for these datasets imply their public availability. |
| Dataset Splits | Yes | For all datasets, we use a three-fold cross-validation and report the mean performance over the validation set. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run the experiments. It only details the network architecture and training patch sizes. |
| Software Dependencies | No | The paper mentions using a 'deep neural network' and 'cross-entropy loss' but does not specify any software libraries or versions (e.g., PyTorch 1.9, TensorFlow 2.x) with version numbers. |
| Experiment Setup | Yes | Our network contains six trainable weight layers, four convolutional layers and two fully connected layers. The first, second and fourth convolutional layers are followed by a single max pooling layer of size 2 × 2 and stride 2 by the end of the layer. Particularly, because of the computational complexity, we use a patch size of 65 × 65 during all the training process. ... For convenience, we drop the weight of cross entropy loss and weight the topological loss with λ. ... In general, λ is at the magnitude of 1/10000. |