Update README.md

Author: esgomezm

README.md

@@ -8,17 +8,15 @@
 
 ## Project Overview
 
-This repository contains the code and notebooks for preparing ground truth data for training, training a 3D Stardist model, and then use it on unseen data for **Sparse bacteria segmentation in tissue samples**. 
-The project implements deep learning approaches for automated detection and segmentation of bacterial cells in complex tissue environments using 3D microscopy data. 
+This repository contains the code and notebooks for **sparse bacteria instance segmentation in 3D tissue samples**. The main approach consists on training a 3D StarDist model to process 3D microscopy images of tissue. The notebooks provide functionalities for preparing the training data, training a 3D Stardist model, and use it to process unseen data. 
 Developed as part of the [AI4Life project](https://ai4life.eurobioimaging.eu), it uses data provided by Sebastien Herbert from University of Basel in Switzerland.
-All images used in this tutorial are licensed under **CC-BY**. If any of the instructions are not working, please [open an issue]
+All images used in this tutorial are licensed under **CC-BY**. If any of the instructions are not working, please [open an issue].
 
 
 ## 🎯 Objective
 
 The main goal is to develop robust AI models for:
-- Automated segmentation of *Staphylococcus aureus* bacteria in tissue samples
-- 3D cell detection and counting in microscopy images
+- Automated 3D segmentation of *Staphylococcus aureus* bacteria in tissue samples
 - Quality control and validation of segmentation results
 
 ## 📁 Repository Structure
@@ -48,10 +46,14 @@ AI4LIfe_OC_StaphInTissue/
 
 1. Follow the [DL4MicEverywhere](https://github.com/HenriquesLab/DL4MicEverywhere) installation instructions and load the Stardist 3D ZeroCostDL4Mic environment.
 
-2. In the Jupyter notebook interface, clone this repository:
+2. In the Jupyter notebook interface opened by DL4MicEverywhere, clone this repository:
 ```bash
 git clone https://github.com/ai4life-opencalls/****.git
 ```
+** Note: To reproduce the pipeline outside DL4MicEverywhere, please use the notebooks under the Stardist 3D ZeroCostDL4Mic environment, which can be set up with `env_requirements.txt`:
+```bash
+conda create --name staphintissue --file env_requirements.txt
+```
 
 ## 📊 Workflow
 
@@ -63,23 +65,23 @@ The workflow was designed to use the available notebooks in the following order:
 - Allows for informed dataset splitting into training and testing sets by the user
 
 ### 2. Label Refinement (`0.2_label_watershed.ipynb`)
+(Note: only needed when bacteria clusters are manually annotated as a unit rather than each individual bacterial cell having a unique label)
 - Handling of touching/overlapping bacteria labeled as a single instance
 - Application of watershed algorithm for improved instance labeling
 
 ### 3. Data Preprocessing (`0.3_normalize_and_crop.ipynb`)
-- Image normalization and cropping, needed **only for the training data** due to its large size
-- Cropping considers regions with bacteria to balance the ratio of empty background regions and optimize training efficiency
-- This step is required to avoid the time consuming processing in the dataloaders during training, this step in not needed for the testing data nor inference as it is integrated in the prediction functions
+- Image normalization and cropping, needed **only for the training data**. This step is required due to the large size of the images.
+- Cropping considers regions with bacteria to balance the ratio of empty background regions and optimize training efficiency. This step is required to speed up data loading during training, as normalisation should be done sample-wise and not patch-wise.
+- This step is **not needed for the test data nor during inference**, as it is integrated in the prediction functions
 
 ### 4. Model Training (`StarDist_3D_DL4MicEverywhere_Modified_AI4LifeOC.ipynb`)
 - DL4MicEverywhere StarDist 3D model training, validation, and inference
-- Custom modifications for large image handling
-
+- The notebook contains custom modifications to handle large 3D images.
 
 ## 📈 Results
 
-The model provides:
-- Segmentation masks for individual bacteria
+This pipeline provides:
+- Instance segmentation masks for individual bacteria
 - Quality metrics and validation reports
 - Interactive visualization of results
 
@@ -88,8 +90,8 @@ The model provides:
 
 - Schmidt, Uwe, et al. "Cell detection with star-convex polygons." International conference on medical image computing and computer-assisted intervention. Cham: Springer International Publishing, 2018.
 - Weigert, Martin, et al. "Star-convex polyhedra for 3D object detection and segmentation in microscopy." Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2020.
-- Von Chamier, Lucas, et al. "Democratising deep learning for microscopy with ZeroCostDL4Mic." Nature communications 12.1 (2021): 2276.
-- Hidalgo-Cenalmor, Iván, et al. "DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible." Nature methods 21.6 (2024): 925-927.
+- Von Chamier, Lucas, et al. "Democratising deep learning for microscopy with ZeroCostDL4Mic." Nature Communications 12.1 (2021): 2276.
+- Hidalgo-Cenalmor, Iván, et al. "DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible." Nature Methods 21.6 (2024): 925-927.
 
 ## Acknowledgements
 AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.