A pre-trained model for volumetric (3D) annotation of the spleen from CT image.
Note: The 4.1 version of this model is only compatible with the 4.1 version of the Clara Train SDK container
The model is trained to annotate the spleen based on 1 input CT abdominal scan and 6 extreme points. The annotation algorithm is described in . The network architecture is described in . The workflow is described as below.
This model utilized a similar architecture as Residual U-net . The training was performed with the following:
The training data is the Task09_Spleen.tar from MSD challenge http://medicaldecathlon.com/.
The provided labeled data was partitioned, based on our own split, into training (31 studies), validation (4 studies) and testing (6 studies) datasets.
The model was trained with 200 cases with our own split, as shown in the datalist json file in config folder. The achieved Dice scores on the validation and testing data are:
Spleen : 0.954 (testing)
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: 2 channel
Augmentation for training:
Output: 1 channel spleen
This training and inference pipeline was developed by NVIDIA. It is based on a segmentation model developed by NVIDIA researchers. 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.
 Maninis, Kevis-Kokitsi, et al. "Deep extreme cut: From extreme points to object segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. https://arxiv.org/abs/1711.09081.
 Kerfoot E., Clough J., Oksuz I., Lee J., King A.P., Schnabel J.A. (2019) Left-Ventricle Quantification Using Residual U-Net. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_40.
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