Abstract

BackgroundGrayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. However, model-driven methods like thresholding usually suffer from wrong segmentation and noises regions because different grayscale images have distinct intensity distribution property thus pre-processing is always demanded. While data-driven methods with deep learning like encoder-decoder networks always are always accompanied by complex architectures which require amounts of training data.MethodsCombining thresholding method and deep learning, this paper presents a novel method by using 2D&3D object detection technologies. First, interest regions contain segmented object are determined with fine-tuning 2D object detection network. Then, pixels in cropped images are turned as point cloud according to their positions and grayscale values. Finally, 3D object detection network is applied to obtain bounding boxes with target points and boxes’ bottoms and tops represent thresholding values for segmentation. After projecting to 2D images, these target points could composite the segmented object.ResultsThree groups of grayscale medical images are used to evaluate the proposed image segmentation method. We obtain the IoU (DSC) scores of 0.92 (0.96), 0.88 (0.94) and 0.94 (0.94) for segmentation accuracy on different datasets respectively. Also, compared with five state of the arts and clinically performed well models, our method achieves higher scores and better performance.ConclusionsThe prominent segmentation results demonstrate that the built method based on 2D&3D object detection with deep learning is workable and promising for segmentation task of grayscale medical images.

Highlights

  • Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis

  • According to above strategy, we propose the grayscale medical image segmentation method based on 2D&3D object detection

  • Inspired by two-stage 2D object detection methods, we present a novel two-stage 3D object detection method, which is operated on pixel-features point cloud

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Summary

Introduction

Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Modeldriven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. Model-driven methods like thresholding usually suffer from wrong segmentation and noises regions because different grayscale images have distinct intensity distribution property pre-processing is always demanded. Ge et al BMC Medical Imaging (2022) 22:33 hemorrhage segmentation [10, 11], etc They could be considered to divide origin images into several sub regions for picking up some crucial objects and extracting interesting features which improve the computer aided diagnostic efficiency. Many model-driven methods for medical image segmentation, including thresholding, clustering, and region growing, were presented in particular before the widespread application of deep learning [12]. Segmentation objects often occupied only parts of whole images and pixels of different objects may share same intensity values, so noises could appear if image segmentation was applied overall

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