Semantic Segmentation of Glomeruli in Human Kidney Tissue Images

Ishrak, Sheikh Sakib; Khan, Md. Sakif; Imam, Ahmad

dc.contributor.author	Ishrak, Sheikh Sakib
dc.contributor.author	Khan, Md. Sakif
dc.contributor.author	Imam, Ahmad
dc.date.accessioned	2022-04-08T03:03:05Z
dc.date.available	2022-04-08T03:03:05Z
dc.date.issued	2021-03-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1310
dc.description	Supervised by Md. Redwan Karim Sony, Lecturer, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704. Bangladesh	en_US
dc.description.abstract	For reliable disease diagnosis in renal pathology, accurate glomerular microscopic medical image segmentation is important. In this study, we work on a pixel-level labeled glomerular microscopic medical image segmentation dataset, the HuBMAP kidney dataset, and improve a novel pipeline for implementing automatic segmen- tation of glomerular microscopic medical images. In our thesis work, we have tried using many variations of segmentation models, encoders, feature extractors and explored their potentials for semantic segmentation of glomeruli. In our proposed approach, we used the network architecture which gives the most promising re- sult on the dataset, consisting of LinkNet with E cientNet as modi ed encoder block, pretrained on ImageNet. Here the Compound Scaling provides better per- formance without compromising the e ciency. This pipeline outperformed other models that we have experimented with and allowed better performance than previous non deep learning based methodologies of glomerular identi cation.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Semantic Segmentation of Glomeruli in Human Kidney Tissue Images	en_US
dc.type	Thesis	en_US