Exploring Efficient Volumetric Medical Image Segmentation Using 2.5D Method: An Empirical Study

by   Yichi Zhang, et al.

With the unprecedented developments in deep learning, many methods are proposed and have achieved great success for medical image segmentation. However, unlike segmentation of natural images, most medical images such as MRI and CT are volumetric data. In order to make full use of volumetric information, 3D CNNs are widely used. However, 3D CNNs suffer from higher inference time and computation cost, which hinders their further clinical applications. Additionally, with the increased number of parameters, the risk of overfitting is higher, especially for medical images where data and annotations are expensive to acquire. To issue this problem, many 2.5D segmentation methods have been proposed to make use of volumetric spatial information with less computation cost. Despite these works lead to improvements on a variety of segmentation tasks, to the best of our knowledge, there has not previously been a large-scale empirical comparison of these methods. In this paper, we aim to present a review of the latest developments of 2.5D methods for volumetric medical image segmentation. Additionally, to compare the performance and effectiveness of these methods, we provide an empirical study of these methods on three representative segmentation tasks involving different modalities and targets. Our experimental results highlight that 3D CNNs may not always be the best choice. Besides, although all these 2.5D methods can bring performance gains to 2D baseline, not all the methods hold the benefits on different datasets. We hope the results and conclusions of our study will prove useful for the community on exploring and developing efficient volumetric medical image segmentation methods.


page 2

page 4

page 5


SAM3D: Segment Anything Model in Volumetric Medical Images

Image segmentation is a critical task in medical image analysis, providi...

3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation

Despite that the segment anything model (SAM) achieved impressive result...

How Can CNNs Use Image Position for Segmentation?

Convolution is an equivariant operation, and image position does not aff...

CapsNet for Medical Image Segmentation

Convolutional Neural Networks (CNNs) have been successful in solving tas...

Roughness Index and Roughness Distance for Benchmarking Medical Segmentation

Medical image segmentation is one of the most challenging tasks in medic...

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Despite the remarkable performance of deep learning methods on various t...

Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation

Despite the recent improvements in overall accuracy, deep learning syste...

Please sign up or login with your details

Forgot password? Click here to reset