Thank you for the excellent work of digital image analysis (DIA) from you and your team.
I am working on a methology comparison of different DIA software. In my opinion, the most important function of DIA software is tissue segment and cell segment (including cell detect and phenotyping). Other function such as spatial analysis or acquirment of mean fluorescence intensity is all based on this. Many kinds of excellent DIA software are based on machine learning of artificial intelligence (AI), so I want to know :
1.What is the main difference between these kinds of DIA software, such as HALO, Inform, FCS Express and Fiji, in tissue segment and cell segment. Is the algorithm? Or the number of parameters selected for the analysis?
2.For Qupath, I read your excellent paper named “QuPath: Open source software for digital pathology image analysis”. In my opinion, the tissue segment is based on watershed strategy. And cell detection is based on random trees classifier. I don’t know if it is right, and how many parameters did you select for analysis?
3.Although different choice of algorithm may get the same results, whether we can say that application of Convolutional neural network (CNN) is the trend of DIA software among all machine learning algorithm, and it may bring us more accuracy results in a shorter time.
I used DIA software to analysis multiplex IF images jsut for one year. And my theoretical knowledge of machine learning is very poor. I think my questions may be primary, I read a lot of papers but didn’t find the answers. So could you please give me some help ? Thank you for your patience.
Hope for your reply.
Dalian Medical University, China.