D
Debdoot Sheet
Researcher at Indian Institute of Technology Kharagpur
Publications - 129
Citations - 2813
Debdoot Sheet is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Convolutional neural network & Segmentation. The author has an hindex of 19, co-authored 121 publications receiving 1824 citations. Previous affiliations of Debdoot Sheet include Jadavpur University & Ludwig Maximilian University of Munich.
Papers
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Journal ArticleDOI
ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks.
Abhijit Guha Roy,Sailesh Conjeti,Sri Phani Krishna Karri,Debdoot Sheet,Amin Katouzian,Christian Wachinger,Nassir Navab +6 more
TL;DR: A new fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans, validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods.
Journal ArticleDOI
Brightness preserving dynamic fuzzy histogram equalization
TL;DR: The modified technique, called Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE), uses fuzzy statistics of digital images for their representation and processing, resulting in improved performance.
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CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation.
A. Emre Kavur,N. Sinem Gezer,Mustafa Baris,Sinem Aslan,Pierre-Henri Conze,Vladimir Groza,Duc Duy Pham,Soumick Chatterjee,Philipp Ernst,Savas Ozkan,Bora Baydar,Dmitrii Lachinov,Shuo Han,Josef Pauli,Fabian Isensee,Matthias Perkonigg,Rachana Sathish,Ronnie Rajan,Debdoot Sheet,Gurbandurdy Dovletov,Oliver Speck,Andreas Nürnberger,Klaus H. Maier-Hein,Gozde Bozdagi Akar,Gozde Unal,Oğuz Dicle,M. Alper Selver +26 more
TL;DR: The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance, but the best MSSD performance remains limited, and multi-tasking DL models designed to segment all organs are observed to perform worse compared to organ-specific ones.
Journal ArticleDOI
IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge
Prasanna Porwal,Prasanna Porwal,Samiksha Pachade,Manesh Kokare,Girish Deshmukh,Jaemin Son,Woong Bae,Lihong Liu,Jianzong Wang,Xinhui Liu,Liangxin Gao,Tian Bo Wu,Jing Xiao,Fengyan Wang,Baocai Yin,Yunzhi Wang,Gopichandh Danala,Linsheng He,Yoon-Ho Choi,Yeong Chan Lee,Sang Hyuk Jung,Zhongyu Li,Xiaodan Sui,Junyan Wu,Xiaolong Li,Ting Zhou,Janos Toth,Agnes Baran,Avinash Kori,Sai Saketh Chennamsetty,Mohammed Safwan,Varghese Alex,Xingzheng Lyu,Li Cheng,Qinhao Chu,Pengcheng Li,Xin Ji,Sanyuan Zhang,Shen Yaxin,Ling Dai,Oindrila Saha,Rachana Sathish,Tânia Melo,Teresa Araújo,Balazs Harangi,Bin Sheng,Ruogu Fang,Debdoot Sheet,Andras Hajdu,Yuanjie Zheng,Ana Maria Mendonça,Shaoting Zhang,Aurélio Campilho,Bin Zheng,Dinggang Shen,Luca Giancardo,Gwenole Quellec,Fabrice Meriaudeau +57 more
TL;DR: The set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD), which received a positive response from the scientific community, have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
Posted Content
Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images.
TL;DR: This work presents a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images using an ensemble of deep convolutional neural networks to segment vessel and non-vessel areas of a color fundus image.