Update README for 110 to include NNCF notebook (openvinotoolkit#578)

notebooks/110-ct-segmentation-quantize/README.md

@@ -4,21 +4,30 @@
 
 ## What's Inside
 
-This folder contains three notebooks that show how to train,
+This folder contains four notebooks that show how to train,
 optimize, quantize and show live inference on a [MONAI](https://monai.io/) segmentation model with
 [PyTorch Lightning](https://pytorchlightning.ai/) and OpenVINO:
 
-1. [Data Preparation for 2D Segmentation of 3D Medical Data](data-preparation-ct-scan.ipynb)
-2. [Train a 2D-UNet Medical Imaging Model with PyTorch Lightning](pytorch-monai-training.ipynb)
-3. [Convert and Quantize a UNet Model and Show Live Inference](110-ct-segmentation-quantize.ipynb)
 
+1\. [Data Preparation for 2D Segmentation of 3D Medical Data](data-preparation-ct-scan.ipynb)
 
-We provided a pretrained model and a subset of the dataset for the quantization notebook, so it is not required to run the data preparation and training notebooks before running the quantization tutorial.
+2\. [Train a 2D-UNet Medical Imaging Model with PyTorch Lightning](pytorch-monai-training.ipynb)
 
-The quantization tutorial shows how to:
+3a. [Convert and Quantize a UNet Model and Show Live Inference using POT](110-ct-segmentation-quantize.ipynb)
+
+3b. [Convert and Quantize a UNet Model and Show Live Inference using NNCF](110-ct-segmentation-quantize-nncf.ipynb)
+
+The main difference between the POT and NNCF quantization notebooks is that
+NNCF performs quantization within the PyTorch framework, while POT performs
+quantization after the PyTorch model has been converted to OpenVINO IR format.
+We provide a pretrained model and a subset of the dataset for the quantization
+notebooks, so it is not required to run the data preparation and training
+notebooks before running the quantization tutorials.
+
+The quantization tutorials show how to:
 
 - Convert an ONNX model to OpenVINO IR with [Model Optimizer](https://docs.openvino.ai/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
-- Quantize a model with OpenVINO's [Post-Training Optimization Tool](https://docs.openvino.ai/latest/pot_compression_api_README.html) API
+- Quantize a model with OpenVINO's [Post-Training Optimization Tool](https://docs.openvino.ai/latest/pot_compression_api_README.html) API or [NNCF](https://github.com/openvinotoolkit/nncf)
 - Evaluate the F1 score metric of the original model and the quantized model
 - Benchmark performance of the original model and the quantized model
 - Show live inference with OpenVINO's async API and MULTI plugin