More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation

08/20/2019
by   Yunguan Fu, et al.
21

Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications. With a laparoscopic liver image segmentation application, we investigate the performance impact by altering the quantities of labelled and unlabelled training data, using a semi-supervised segmentation algorithm based on the mean teacher learning paradigm. We first report a significantly higher segmentation accuracy, compared with supervised learning. Interestingly, this comparison reveals that the training strategy adopted in the semi-supervised algorithm is also responsible for this observed improvement, in addition to the added unlabelled data. We then compare different combinations of labelled and unlabelled data set sizes for training semi-supervised segmentation networks, to provide a quantitative example of the practically useful trade-off between the two data planning strategies in this surgical guidance application.

READ FULL TEXT
research
08/09/2023

SegMatch: A semi-supervised learning method for surgical instrument segmentation

Surgical instrument segmentation is recognised as a key enabler to provi...
research
12/31/2017

Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation

A novel multi-atlas based image segmentation method is proposed by integ...
research
04/20/2021

More Than Meets The Eye: Semi-supervised Learning Under Non-IID Data

A common heuristic in semi-supervised deep learning (SSDL) is to select ...
research
07/13/2021

Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift

Scarcity of high quality annotated images remains a limiting factor for ...
research
08/27/2022

Weakly and Semi-Supervised Detection, Segmentation and Tracking of Table Grapes with Limited and Noisy Data

Detection, segmentation and tracking of fruits and vegetables are three ...
research
11/06/2022

Learning to Annotate Part Segmentation with Gradient Matching

The success of state-of-the-art deep neural networks heavily relies on t...
research
05/18/2022

SemiCurv: Semi-Supervised Curvilinear Structure Segmentation

Recent work on curvilinear structure segmentation has mostly focused on ...

Please sign up or login with your details

Forgot password? Click here to reset