From 805c1f80d60a7401f50bdce45d49b289858e4923 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 23 Dec 2024 10:10:53 +0100 Subject: [PATCH 1/2] Version bump 0.2.2 --- README.md | 16 ++++++++++------ napari_cellseg3d/__init__.py | 2 +- napari_cellseg3d/code_plugins/plugin_helper.py | 2 +- setup.cfg | 2 +- 4 files changed, 13 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 09252dab..fb98e663 100644 --- a/README.md +++ b/README.md @@ -52,21 +52,25 @@ The strength of our approach is we can match supervised model performance with p ![FIG1 (1)](https://github.com/user-attachments/assets/0d970b45-79ff-4c58-861f-e1e7dc9abc65) -**Figure 1. Performance of 3D Semantic and Instance Segmentation Models.** -**a:** Raw mesoSPIM whole-brain sample, volumes and corresponding ground truth labels from somatosensory (S1) and visual (V1) cortical regions. +**Figure 1. Performance of 3D Semantic and Instance Segmentation Models.** +**a:** Raw mesoSPIM whole-brain sample, volumes and corresponding ground truth labels from somatosensory (S1) and visual (V1) cortical regions. **b:** Evaluation of instance segmentation performance for baseline thresholding-only, supervised models: Cellpose, StartDist, SwinUNetR, SegResNet, and our self-supervised model WNet3D over three data subsets. F1-score is computed from the Intersection over Union (IoU) with ground truth labels, then averaged. Error bars represent 50% Confidence Intervals -(CIs). -**c:** View of 3D instance labels from supervised models, as noted, for visual cortex volume in b evaluation. +(CIs). +**c:** View of 3D instance labels from supervised models, as noted, for visual cortex volume in b evaluation. **d:** Illustration of our WNet3D architecture showcasing the dual 3D U-Net structure with our modifications. ## News -**New version: v0.2.1** +**New version: v0.2.2** -- v0.2.1: +- v0.2.2: + - Updated Colab notebooks for training and inference! + - New models available in inference demo notebook + - CRF optional post-processing adjustments +- v0.2.2: - Updated plugin default behaviors across the board to be more readily applicable to demo data - Threshold value in inference is now automatically set by default according to performance on demo data on a per-model basis - Added a grid search utility to find best thresholds for supervised models diff --git a/napari_cellseg3d/__init__.py b/napari_cellseg3d/__init__.py index c2131a65..7a1ba361 100644 --- a/napari_cellseg3d/__init__.py +++ b/napari_cellseg3d/__init__.py @@ -1,3 +1,3 @@ """napari-cellseg3d - napari plugin for cell segmentation.""" -__version__ = "0.2.1" +__version__ = "0.2.2" diff --git a/napari_cellseg3d/code_plugins/plugin_helper.py b/napari_cellseg3d/code_plugins/plugin_helper.py index 43a91b33..27a90214 100644 --- a/napari_cellseg3d/code_plugins/plugin_helper.py +++ b/napari_cellseg3d/code_plugins/plugin_helper.py @@ -44,7 +44,7 @@ def __init__(self, viewer: "napari.viewer.Viewer"): self.logo_label.setToolTip("Open Github page") self.info_label = ui.make_label( - f"You are using napari-cellseg3d v.{'0.2.1'}\n\n" + f"You are using napari-cellseg3d v.{'0.2.2'}\n\n" f"Plugin for cell segmentation developed\n" f"by the Mathis Lab of Adaptive Motor Control\n\n" f"Code by :\nCyril Achard\nMaxime Vidal\nJessy Lauer\nMackenzie Mathis\n" diff --git a/setup.cfg b/setup.cfg index 7dd98e41..ebac8d9e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = napari_cellseg3d -version = 0.2.1 +version = 0.2.2 [options] packages = find: From 2d89a808d0abcb1086b0524269521a95beebf213 Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Mon, 23 Dec 2024 14:09:49 +0100 Subject: [PATCH 2/2] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index fb98e663..0089ac3a 100644 --- a/README.md +++ b/README.md @@ -67,10 +67,10 @@ F1-score is computed from the Intersection over Union (IoU) with ground truth la **New version: v0.2.2** - v0.2.2: - - Updated Colab notebooks for training and inference! - - New models available in inference demo notebook - - CRF optional post-processing adjustments -- v0.2.2: + - Updated the Colab Notebooks for training and inference + - New models available in the inference demo notebook + - CRF optional post-processing adjustments (and pip install directly) +- v0.2.1: - Updated plugin default behaviors across the board to be more readily applicable to demo data - Threshold value in inference is now automatically set by default according to performance on demo data on a per-model basis - Added a grid search utility to find best thresholds for supervised models