Machine learning methods for accurate delineation of tumors in PET images
This work addresses the need for accurate and reproducible tumor delineation in PET imaging for oncology diagnostics and treatment planning, but it appears incremental as it applies existing methods without claiming major breakthroughs.
The researchers tackled the problem of automated tumor delineation in PET images, which is crucial for cancer treatment, by applying 3D implementations of fuzzy c-means, deep convolutional neural networks, and a dictionary model, but no concrete results or numbers are provided in the abstract.
In oncology, Positron Emission Tomography imaging is widely used in diagnostics of cancer metastases, in monitoring of progress in course of the cancer treatment, and in planning radiotherapeutic interventions. Accurate and reproducible delineation of the tumor in the Positron Emission Tomography scans remains a difficult task, despite being crucial for delivering appropriate radiation dose, minimizing adverse side-effects of the therapy, and reliable evaluation of treatment. In this piece of research we attempt to solve the problem of automated delineation of the tumor using 3d implementations of the spatial distance weighted fuzzy c-means, the deep convolutional neural network and a dictionary model. The methods, in diverse ways, combine intensity and spatial information.