644zhang / mfcn Goto Github PK

The histogram equalization can be implemented with the Matlab built-in function, e.g.,:
J = histeq(img);

We provide a MFCN implement of the Caffe version. In this experiment, we train the model from scratch. In effect,
users can finetune their own model with our trained model , which can improve the training speed significantly.
Once we have trained the model, we can use the trained model to segment new images. This python script adopts a
patch-based testing method, in which the input image (i.e., size of 1792 × 2048) is sliced into smaller patches
(i.e., size of 640 × 640) to be run on GPU. However, the whole (i.e., size of 1792 × 2048) image can be segmented
on a CPU with MFCN model immediately, although this is relatively slow.

After obtaining binary segmentation maps, to record information for each vesicle, e.g., area, radius, and gray-value,
we adopt a watershed-based instance segmentation and save each vesicle’s contour as .roi files, which can be read by
ImageJ software. We provide Matlab scripts , first running watershed_for_dbwts.m to record information for each vesicle,
and then running freeroisaveapply.m to save the .roi files. These .roi files can be read and analyzed by ImageJ software.

If you need the dataset or have any questions, please contact with [email protected]