Segmentation of Multiple Myeloma Cells Using Feature Selection Pyramid Network and Semantic Cascade Mask RCNN

Xinyun Qiu, Haijun Lei, Hai Xie, Baiying Lei
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引用次数: 2

Abstract

Multiple myeloma (MM) is a blood cancer that develops when plasma cells expand abnormally in the bone marrow. The early detection of MM is beneficial for accurate treatment in time and draws increasing recognition. There are several methods to detect myeloma cells in bone marrow, such as using microscopic analysis based on the aspirate slide images. In this paper, we propose a deep learning framework called the semantic cascade Mask RCNN for the detection and segmentation of myeloma cells. The framework is also integrated with the proposed feature selection pyramid network, which is a simple and effective module to improve the segmentation performance. The mask aggregation module refines and merges the high certainty instance masks into a single segmentation map and combines the results from the extra semantic segmentation branch to generate better predictions. The extensive experiments on the SegPC-2021 Challenge dataset demonstrate that the proposed method achieves a promising performance.
基于特征选择金字塔网络和语义级联掩模的多发性骨髓瘤细胞分割
多发性骨髓瘤(MM)是一种血癌,当浆细胞在骨髓中异常扩张时发展而来。MM的早期发现有利于及时准确治疗,受到越来越多的重视。有几种方法可以检测骨髓中的骨髓瘤细胞,如基于吸片图像的显微分析。在本文中,我们提出了一种称为语义级联掩模RCNN的深度学习框架,用于骨髓瘤细胞的检测和分割。该框架与所提出的特征选择金字塔网络相结合,是提高分割性能的一个简单有效的模块。掩码聚合模块将高确定性实例掩码细化并合并到单个分割图中,并结合额外语义分割分支的结果来生成更好的预测。在SegPC-2021挑战数据集上的大量实验表明,该方法取得了良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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