Efficient and Generic Interactive Segmentation Framework to Correct Mispredictions during Clinical Evaluation of Medical Images
IIIT Hyderabad IIT Delhi
[Code] [Paper] [Supplementary] [Demo Video] [Test Sets]
We propose a novel approach to generate annotations for medical images of several modalities in a semi-automated manner. In contrast to the existing methods, our method can be implemented using any semantic segmentation method for medical images, allows correction of multiple labels at the same time and addition of missing labels.
Some additional details have been provided which we were unable to put in the paper due to space constraints.
Multiple Label Segmentation
Our approach has the capability to interactively correct the segmentation of multiple labels at the same time
Missing Label Segmentation
Our method has the capability to add labels missed at the initial segmentation.
Unseen Organ Segmentation
We can perform interactive segmentation of organs for which the pre-trained model was not trained for.
The details of the networks used in our paper has been given here.
The network is based on the DRIU architecture. It is a cascaded architecture where the liver is segmented first followed by the lesion.
A multiple branch network has been proposed which does nuclear instance segmentation and classification at the same time. The horizontal and vertical distances of nuclear pixels between their centers of masses are leveraged to separate the clustered cells.
An autofocus layer for semantic segmentation has been proposed here. The autofocus layer is used to change the size of the receptive fields which is used to obtain features at various scales. The convolutional layers are paralellized with an attention mechanism.