Pytorch implementation of our modified encoder-decoder framework
This repository contains code to train state-of-the-art surgical instruments segmentation networks as described in this paper: U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument. This architecture is trained on MICCAI EndoVIs 2017 sub-challenge.
Our code is compatible with python 3.7 or onward.
We depend on some python packages which need to be installed by the user:
- PyTorch
- tqdm
- SimpleITK
- sklearn
- numpy
- S. M. Kamrul Hasan ([sh3190@rit.edu])
- Cristian A. Linte
U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instruments from Laparoscopic Images EMBC 2019, Oral
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S. M. Kamrul Hasan Rochester Institute of Technology
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Cristian A. Linte Rochester Institute of Technology
With the advent of robot-assisted surgery, there has been a paradigm shift in medical technology for minimally invasive surgery. However, it is very challenging to track the position of the surgical instruments in a surgical scene, and accurate detection & identification of surgical tools is paramount. Deep learning-based semantic segmentation in frames of surgery videos has the potential to facilitate this task. In this work, we modify the U-Net architecture by introducing a pre-trained encoder and re-design the decoder part, by replacing the transposed convolution operation with an upsampling operation based on nearest-neighbor (NN) interpolation. To further improve performance, we also employ a very fast and flexible data augmentation technique. We trained the framework on 8 x 225 frame sequences of robotic surgical videos available through the MICCAI 2017 EndoVis Challenge dataset and tested it on 8 x 75 frame and 2 x 300 frame videos. Using our U-NetPlus architecture, we report a 90.20% DICE for binary segmentation, 76.26% DICE for instrument part segmentation, and 46.07% for instrument type (i.e., all instruments) segmentation, outperforming the results of previous techniques implemented and tested on these data.
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For both training and validation, we used the Robotic instruments dataset from the sub-challenge of MICCAI 2017 Endoscopic Vision Challenge [22]. The training dataset has 8 × 225 frame sequences with 2 Hz frame rate of high resolution stereo camera images collected from a da Vinci Xi surgical system during laparoscopic cholecystectomy procedures. The frames were re-sampled from 30 Hz video to 2 Hz to avoid any redundancy issues. A stereo camera was used to capture the video sequences that consists of the left and right eye views with resolution of 1920 × 1080 in RGB format. In each frame, the articulated parts of the robotic surgical instrument consisting of a rigid shaft, an articulated wrist, and claspers, have been manually labeled by expert clinicians. The test set has 8×75 frame sequences and 2×300 frame videos. The challenge is to segment 7 classes such as prograsp forceps, needle driver, vessel sealer, grasping retractor etc. \
Modified U-Net with batch-normalized VGG11 as an encoder. Each box corresponds to a multi-channel featuring a map passing through a series of transformations. It consists of both an upsampling and a downsampling path and the height of the box represents the feature map resolution, while the width represents number of channels. Cyan arrows represent the max-pooling operation, whereas light-green arrows represent skip connections that transfer information from the encoder to the decoder. Red upward arrows represent the decoder which consists of nearest-neighbor upsampling with a scale factor of 2 followed by 2 convolution layers and a ReLU activation function.
Example images of applying both affine and elastic transformation in albumentations library for data augmentation.
Qualitative comparison of binary segmentation, instrument part and instrument type segmentation result and their overlay onto the native endoscopic images of the MICCAI 2017 EndoVis video dataset yielded by four different frameworks: U-Net, U-Net+NN, TernausNet, and U-NetPlus.
Attention results: U-NetPlus “looks” at a focused target region, whereas U-Net, U-Net+NN and TernausNet appear less “focused”, leading to less accurate segmentation.
If you find our work useful in your research, please consider citing:
@inproceedings{hasan2019u,
title={U-NetPlus: A modified encoder-decoder U-Net architecture for semantic and instance segmentation of surgical instruments from laparoscopic images},
author={Hasan, SM Kamrul and Linte, Cristian A},
booktitle={2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
pages={7205--7211},
year={2019},
organization={IEEE}
}
Coming soon!
Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award No. R35GM128877 and by the Office of Advanced Cyber infrastructure of the National Science Foundation under Award No. 1808530. .
Contact: S. M. Kamrul Hasan (smkamrulhasan.rit@gmail.com)