Updates README.md

README.md

@@ -4,6 +4,8 @@ A toolkit for semantic segmentation of volumetric data using PyTorch deep learni
 
 ![example workflow](https://github.com/DiamondLightSource/volume-segmantics/actions/workflows/tests.yml/badge.svg) ![example workflow](https://github.com/DiamondLightSource/volume-segmantics/actions/workflows/release.yml/badge.svg)
 
+Volume Segmantics provides a simple command-line interface and API that allows researchers to quickly train a variety of 2D PyTorch segmentation models (e.g.  U-Net, U-Net++, FPN, DeepLabV3+) on their 3D datasets. These models use pre-trained encoders, enabling fast training on small datasets. Subsequently, the library enables using these trained models to segment larger 3D datasets, automatically merging predictions made in orthogonal planes and rotations to reduce artifacts that may result from predicting 3D segmentation using a 2D network.  
+
 Given a 3d image volume and corresponding dense labels (the segmentation), a 2d model is trained on image slices taken along the x, y, and z axes. The method is optimised for small training datasets, e.g a single dataset in between $128^3$ and $512^3$ pixels. To achieve this, all models use pre-trained encoders and image augmentations are used to expand the size of the training dataset.
 
 This work utilises the abilities afforded by the excellent [segmentation-models-pytorch](https://github.com/qubvel/segmentation_models.pytorch) library in combination with augmentations made available via [Albumentations](https://albumentations.ai/). Also the metrics and loss functions used make use of the hard work done by Adrian Wolny in his [pytorch-3dunet](https://github.com/wolny/pytorch-3dunet) repository.