{"id":18785270,"url":"https://github.com/andreped/pls-net","last_synced_at":"2026-03-12T14:17:14.136Z","repository":{"id":65839625,"uuid":"315624700","full_name":"andreped/PLS-Net","owner":"andreped","description":"Reimplementation of the PLS-Net architecture used for lung lobe segmentation in CT","archived":false,"fork":false,"pushed_at":"2023-04-16T20:03:54.000Z","size":427,"stargazers_count":8,"open_issues_count":0,"forks_count":2,"subscribers_count":3,"default_branch":"main","last_synced_at":"2026-02-06T20:38:25.693Z","etag":null,"topics":["ct","deep-learning","efficient","lightning","lobe","lung","lung-lobe","pls-net","plsnet","pytorch","pytorch-lightning","segmentation","separable-convolutions","u-net"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/andreped.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-11-24T12:25:41.000Z","updated_at":"2023-11-26T11:36:25.000Z","dependencies_parsed_at":null,"dependency_job_id":"5c7b4d98-6af7-47fb-8dd3-eb645050f865","html_url":"https://github.com/andreped/PLS-Net","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/andreped/PLS-Net","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andreped%2FPLS-Net","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andreped%2FPLS-Net/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andreped%2FPLS-Net/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andreped%2FPLS-Net/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/andreped","download_url":"https://codeload.github.com/andreped/PLS-Net/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andreped%2FPLS-Net/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30428015,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-12T14:00:25.264Z","status":"ssl_error","status_checked_at":"2026-03-12T13:59:52.690Z","response_time":114,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ct","deep-learning","efficient","lightning","lobe","lung","lung-lobe","pls-net","plsnet","pytorch","pytorch-lightning","segmentation","separable-convolutions","u-net"],"created_at":"2024-11-07T20:46:06.357Z","updated_at":"2026-03-12T14:17:14.112Z","avatar_url":"https://github.com/andreped.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"assets/hjelde2020lobe.png\" width=\"800\"\u003e\n\u003ch1 align=\"center\"\u003ePLS-Net (PyTorch)\u003c/h1\u003e\n\u003ch3 align=\"center\"\u003eReimplementation of the PLS-Net architecture used for lung lobe segmentation in CT proposed by Lee et al. (2019).\u003c/h3\u003e\n\n[![test](https://github.com/andreped/PLS-Net/actions/workflows/test.yml/badge.svg)](https://github.com/andreped/PLS-Net/actions/workflows/test.yml)\n[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n[![Paper](https://zenodo.org/badge/DOI/10.1117/1.JMI.8.2.024002.svg)](https://doi.org/10.1117/1.JMI.8.2.024002)\n\u003c/div\u003e\n\nThe implementation was made for this study by [Bouget et al. (2021)](https://doi.org/10.1117/1.JMI.8.2.024002). The original implementation can be found [here](https://arxiv.org/abs/1909.07474).\n\n## [Usage](https://github.com/andreped/PLS-Net#usage)\n\nThe source code was tested in Python 3.6 with CUDA 10.0.\n\n### [Clone repo and install requirements](https://github.com/andreped/PLS-Net#clone-repo-and-install-requirements)\n```\ngit clone git+https://github.com/andreped/PLS-Net\ncd \"PLS-Net\"\npip install -r requirements.txt\n```\n\n### [Define network](https://github.com/andreped/PLS-Net#define-network)\n```\nfrom PLS_pytorch import PLS\nnetwork = PLS()\n```\n\n(Alternatively) in PyTorch-Lightning:\n```\nfrom PLS_lightning import PLS\nnetwork = PLS()\n```\n\nDisclaimer: Note that the Lightning implementation contains some hardcoded setup and Dataloaders, and thus only serves as an example. However, PyTorch implementation should work out-of-the-box.\n\n## [How to cite](https://github.com/andreped/PLS-Net#how-to-cite)\nIf the source code is used in any scientific publication, please, cite the following papers:\n```\n@article{bouget2021code,\n  author = {David Bouget and Andr{\\'e} Pedersen and Sayied Abdol Mohieb Hosainey and Johanna Vanel and Ole Solheim and Ingerid Reinertsen},\n  title = {{Fast meningioma segmentation in T1-weighted magnetic resonance imaging volumes using a lightweight 3D deep learning architecture}},\n  volume = {8},\n  journal = {Journal of Medical Imaging},\n  number = {2},\n  publisher = {SPIE},\n  pages = {024002},\n  keywords = {three-dimensional segmentation, deep learning, meningioma, magnetic resonance imaging, clinical diagnosis, Magnetic resonance imaging, Image segmentation, Tumors, Brain, 3D image processing, 3D modeling, Image resolution, Data modeling, Neural networks, Surgery},\n  year = {2021},\n  doi = {10.1117/1.JMI.8.2.024002},\n  url = {https://doi.org/10.1117/1.JMI.8.2.024002}\n}\n```\n```\n@misc{lee2019plsnet,\n  author = {Lee, Hoileong and Matin, Tahreema and Gleeson, Fergus and Grau, Vicente},\n  title = {{Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images}},\n  publisher = {arXiv},\n  year = {2019},\n  copyright = {arXiv.org perpetual, non-exclusive license},\n  keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},\n  doi = {10.48550/ARXIV.1909.07474},\n  url = {https://arxiv.org/abs/1909.07474}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandreped%2Fpls-net","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandreped%2Fpls-net","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandreped%2Fpls-net/lists"}