Early therapy response assessment of lesions
For therapy response assessment, texture features are input for machine learning a classifier and for using a machine learnt classifier. Rather than or in addition to using formula-based texture features, data driven texture features are derived from training images. Such data driven texture features are independent analysis features, such as features from independent subspace analysis. The texture features may be used to predict the outcome of therapy based on a few number of or even one scan of the patient.
1. A method for therapy response assessment, the method comprising:
obtaining a pre-therapy medical image of a patient, the pre-therapy medical image representing at least one lesion of the patient;
obtaining a post-therapy medical image of the patient, the post-therapy medical image of the patient representing the at least one lesion of the patient;
convolving, by a processor, the pre-therapy and post therapy medical images with a texture feature learned from training images, the texture feature comprising an independent sub-space analysis feature; and
classifying, by the processor, a therapy response of the lesion with a machine-learnt classifier with a result of the convolving as an input feature to the machine-learnt classifier.
2. The method of claim 1 wherein obtaining the pre-therapy and post therapy medical images comprises obtaining computed tomography images.
3. The method of claim 1 wherein obtaining the post-therapy medical image comprises obtaining only the post-therapy medical image or only the post-therapy medical image and one more post therapy medical image, and wherein classifying comprises classifying with the input feature including, for just features from imaging, only features from the pre-therapy and post-therapy medical images.
4. The method of claim 1 wherein convolving comprises convolving with the texture feature and at least two other texture features learned from the training images.
5. The method of claim 1 wherein convolving comprises convolving with the texture feature learned automatically from the training images and labeled ground truths.
6. The method of claim 1 wherein convolving comprises filtering with a kernel defined by the texture feature and summing intensities output from the filtering, the result being a function of the sum of the intensities.
7. The method of claim 1 wherein convolving comprises convolving with the texture feature comprising a training image-based feature such that different training images result in different texture features.
8. The method of claim 1 wherein classifying comprises classifying with a support vector machine or a regression random forest.
9. The method of claim 1 wherein classifying comprises predicting an outcome of continuing therapy after obtaining the post-therapy medical image.
10. The method of claim 1 wherein classifying comprises indicating a likelihood of therapy success or failure for the lesion.
11. The method of claim 1 further comprising identifying regions of interest in the pre-therapy and post-therapy medical images including the lesion and a border of the lesion, and wherein convolving comprises convolving the texture feature with the regions of interest and not with regions outside the regions of interest.
12. The method of claim 1 further comprising:
convolving, by the processor, the pre-therapy and post therapy medical images with mathematical formula-based texture features;
wherein classifying comprises classifying the therapy response with the result and results from the convolving with the mathematical formula-based textures features as an input vector including the input feature.
13. The method of claim 1 wherein convolving comprises spatially filtering the pre-therapy and post therapy medical images with a spatial kernel defined by the texture feature.