mli0603 / EndoVis2018

MICCAI challenge for EndoVis2018

EndoVis2018

MICCAI challenge for EndoVis2018. The challenge focuses on surgical scene segmentation.

Final Report

See pdf for more details.

Demo video

Result

Mini-Data (20% uniform subsampling)

Network	0	1	2	3	4	5	6	7	8	9	10	11	Mean
UNet	0.87	0.86	0.73	0.76	0.82	0.84	0.68	0.85	0.00	0.66	0.88	0.58	0.71
AlbuNet	0.92	0.91	0.80	0.79	0.90	0.90	0.68	0.78	0.00	0.76	0.91	0.71	0.76
AlbuNet+SuperLabel	0.93	0.93	0.82	0.80	0.91	0.90	0.62	0.86	0.00	0.78	0.92	0.77	0.77
DeepLabV3+	0.91	0.93	0.81	0.82	0.94	0.87	0.51	0.60	0.00	0.76	0.92	0.73	0.73
DeepLabv3+SuperLabel	0.93	0.93	0.83	0.79	0.91	0.90	0.64	0.85	0.00	0.79	0.92	0.82	0.78
DeepLabV3+Aug	0.90	0.94	0.80	0.84	0.94	0.84	0.53	0.68	0.00	0.59	0.81	0.81	0.72
DeepLabv3+SuperLabel+Aug	0.94	0.93	0.83	0.81	0.92	0.92	0.64	0.84	0.00	0.81	0.94	0.83	0.78

Sequence sample

Network	0	1	2	3	4	5	6	7	8	9	10	11	Mean
UNet	0.66	0.87	0.76	0.77	0.41	0.22	0.35	0.22	0.00	0.09	0.53	0.00	0.41
AlbuNet	0.69	0.90	0.76	0.78	0.51	0.29	0.38	0.15	0.00	0.23	0.59	0.01	0.44
AlbuNet+SuperLabel	0.75	0.94	0.79	0.84	0.60	0.43	0.43	0.45	0.00	0.45	0.62	0.00	0.53
DeepLabV3+	0.74	0.89	0.76	0.80	0.65	0.29	0.30	0.40	0.00	0.06	0.56	0.00	0.45
DeepLabv3+SuperLabel	0.74	0.92	0.78	0.83	0.64	0.33	0.33	0.39	0.00	0.20	0.59	0.00	0.48

Random sample

Network	0	1	2	3	4	5	6	7	8	9	10	11	Mean
AlbuNet+SuperLabel	0.96	0.96	0.9	0.88	0.96	0.95	0.72	0.9	0	0.83	0.96	0.87	0.82
DeepLabV3+	0.96	0.95	0.89	0.87	0.96	0.96	0.69	0.9	0.37	0.84	0.97	0.86	0.85
DeepLabv3+SuperLabel	0.97	0.96	0.89	0.87	0.96	0.96	0.7	0.9	0.38	0.82	0.96	0.89	0.86

Visual comparison of with/without superlabel

see Comparison.ipynb

Instruction for Tensorboardx

pip install tensorboardX
pip install tensorflow

start tensorboard by tensorboard --logdir=<dir_to_store_log_file>

Example Notebook

1. UNet
2. AlbuNet
3. DeepLabV3+
4. AlbuNet SuperLabel
5. DeepLabV3+ SuperLabel

Architecture

1. UNet
2. AlbuNet
3. DeepLabV3+
4. AlbuNet SuperLabel
5. DeepLabV3+ SuperLabel

Components

1. model_training.py
2. dataset.py
3. dice_loss.py
4. etc.