CROWDSOURCING AND DEEP LEARNING BASED SEGMENTING AND KARYOTYPING OF CHROMOSOMES

The most challenging problems in karyotyping are segmentation and classification of overlapping chromosomes in metaphase spread images. Often chromosomes are bent in different directions with varyingdegrees of bend. Tediousness and time consuming nature of the effort for ground truth creation makes it difficult to scale the ground truth for training phase. The present disclosure provides an end-to-end solution that reduces the cognitive burden of segmenting and karyotyping chromosomes. Dependency on experts is reduced by employing crowdsourcing while simultaneously addressing the issues associated with crowdsourcing. Identified segments through crowdsourcing are pre-processed to improve classification achieved by employing deep convolutional network (CNN). 核型分析中最具挑战的问题是中期扩散图像中重叠染色体的分割和分类。染色体通常以不同的弯曲程度在不同的方向上弯曲。创造基础事实努力的乏味和耗时性使得难以为训练阶段扩展基础事实。本公开提供了减少染色体分割和核型分析的认知负担的端到端解决方案。通过采用众包同时解决与众包相关的问题，减少了对专家的依赖。通过众包识别的片段被预处理以改善通过使用深度卷积网络(CNN)实现的分类。