The trained model can be used with the AIAA server with Slicer, Fovia and OHIF for annotating abdominal organs. The best results are expected on the organs that this model was trained upon, which is mentioned in the Data section. The model maybe applicable to unseen organs and unseen data however the performance of the annotation is not guaranteed.
A 2D UNet  with residual blocks and 32 channels has been used with 5 encoding levels. The network has 7.28 million trainable parameters.
Below is a pipeline indicating dataset preparation where 3D volumes are split into 2D slices. The prepared dataset is utilized for training a Deepgrow  model
The training was performed with the following:
The current training configuration apart from traditional deep learning hyper-parameters is set for 15 click interactions for both training and validation. Click interactions define how many positive and negative clicks are provided for the additional feature maps in simulation before a single step/iteration is taken for the deep learning model for a batch of images. Single and multi-GPU training options are available.
The input size of the image is 512x512 as a 2D slice, and the positive and negative click maps are automatically handled internally. The input size for the training can be varied by modifying the input argument for dataset.py
The training data is from the MICCAI 2015 Challenge: Multi-Atlas Labeling Beyond The Cranial Vault . Link to the challenge data: https://www.synapse.org/#!Synapse:syn3193805/wiki/217789 The dataset has 13 labeled organs and all organs were utilized for training this model. Further details about the data can be found at the aforementioned link. Medical Segmentation Decathlon (MSD) (http://medicaldecathlon.com/) data was also used for testing for Spleen and Liver tasks.
The data must be converted to 1mm x 1mm resolution before training
sh prepare_dataset.sh --help from *MMAR/commands folder to know more options to prepare the dataset for training
Testing was performed on medical segmentation decathlon dataset (http://medicaldecathlon.com/) for tasks of Spleen and Liver. For a random selection of 10 volumes the following Dice scores were achieved. ~0.96 on Spleen, ~0.92 on Liver with 10-15 clicks per 2D slice.
A graph showing the validation mean dice over N Steps/Epochs for Spleen. Any additional information that they might need to know about the graph.
Achieves Dice scores of ~0.96 and ~0.95 for training and validation on a 80/20 split.
The model was validated with NVIDIA hardware and software. For hardware, the model can run on any NVIDIA GPU with memory greater than 16 GB. For software, this model is usable only as part of Transfer Learning & Annotation Tools in Clara Train SDK container. Find out more about Clara Train at the Clara Train Collections on NGC.
Full instructions for the training and validation workflow can be found in our documentation.
Input: 3 channel 2D CT image/slice with normalized intensity in HU and fixed spacing (1 x 1 x 1mm).
The first channel is the image/slice, the other two channels are positive and negative guidance maps that are based on user interaction/clicks
Output: 1 channel
This training and inference pipeline was developed by NVIDIA. This research use only software has not been cleared or approved by FDA or any regulatory agency. Clara pre-trained models are for developmental purposes only and cannot be used directly for clinical procedures.
 Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention 2016 Oct 17 (pp. 424-432). Springer, Cham.
 Landman, B., et al. "Multi-atlas labeling beyond the cranial vault." URL: https://www.synapse.org (2015).
 Sakinis, Tomas, et al. "Interactive segmentation of medical images through fully convolutional neural networks." arXiv preprint arXiv:1903.08205 (2019).
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