Bharat Giddwani

National Institute of Technology Raipur

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Debesh Jha

Northwestern University

Guotai Wang

University of Electronic Science and Technology of China

Imanol Luengo

Medtronic (United Kingdom)

Lyndon da Cruz

University College London

Chinedu Innocent Nwoye

Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie

Feihong Huang

Southwest Medical University

Maria Grammatikopoulou

Medtronic (United Kingdom)

Christos Bergeles

King's College London

Haojie Wang

First Affiliated Hospital of Fujian Medical University

Mobarakol Islam

University College London

Cooperative Institutions

Chinese Academy of Sciences

University College London

Wellcome / EPSRC Centre for Interventional and Surgical Sciences

University of Chinese Academy of Sciences

King's College London

Beijing Academy of Artificial Intelligence

Institute of Automation

Simula Metropolitan Center for Digital Engineering

University of Electronic Science and Technology of China

Imperial College London

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2020 CATARACTS Semantic Segmentation Challenge.

arXiv (Cornell University) (2021)

Imanol Luengo Maria Grammatikopoulou r.Ali Sayadani Nader Mohammadi Chris Walsh Chinedu Innocent Nwoye

Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation for cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by instrument usage annotations. These annotations included frame-level instrument presence information. In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set. The 2020 CATARACTS Semantic Segmentation Challenge, which was a sub-challenge of the 2020 MICCAI Endoscopic Vision (EndoVis) Challenge, presented three sub-tasks to assess participating solutions on anatomical structure and instrument segmentation. Their performance was assessed on a hidden test set of 531 images from 10 videos of the CATARACTS test set.

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CSTA-2P1D UNet: Consecutive Spatio-Temporal Attention for Multi-Scale 3D Pancreas Segmentation

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (2020)

Bharat Giddwani Shruti Pandey Hitesh Tekchandani Shrish Verma

Accurate pancreas segmentation is a crucial task for the diagnosis of diseases as well as for developing modern surgical tools. In this paper, a novel architecture, named Consecutive Spatio-Temporal Attention (CSTA) 2 Plus 1 Dimensional (2P1D) UNet, is proposed for the volumetric segmentation of the pancreas in CT images. CSTA mechanism is inspired by recent advancements in Squeeze & Excitation (SE) module. It recalibrates feature maps, by multiplying learnable spatial and channel-wise attention maps, sequentially. Further, incorporation of Multi-Sale Output (MSO) and Group Normalization (GN) improves the localization performance and training stability of the model, respectively. Moreover, 2P1D convolution is introduced to enhance the segmentation results of our proposed architecture. Smoothed Dice Loss (SDL) is used as a loss function for training of the proposed model. Collectively, a state-of-the-art performance of 83.70%, 87.70%, and 85.60% on dice score, sensitivity, and precision is achieved, verifying the model's effectiveness for the pancreas segmentation.

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Convolution (computer science)

Normalization

10.1109/icccnt49239.2020.9225546

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Deep Dilated V-Net for 3D Volume Segmentation of Pancreas in CT images

Bharat Giddwani Hitesh Tekchandani Shrish Verma

Pancreas segmentation is essential in the medical diagnosis of cancer, pancreatitis, and pancreatic surgeries. Computed tomography (CT) abdominal scans help in the detection of tumors, infections, and other injuries in the pancreas. The treatment of pancreatic tumors begins with surgery, followed by neoadjuvant therapy. However, it is challenging to detect boundaries, due to variable shape, small anatomical structures, and low contrast images of the pancreas in the abdominal CT scans. Recently, Convolutional Neural Networks (CNNs) based deep learning models show significant performance on medical imaging related tasks. However, most of the data available for the evaluation of diseases consists of 3D CT scan volumes. Hence, learning from volumetric data is essential in biomedical applications. V-Net achieves extraordinary performance in various medical datasets consists of 3D scan. In this paper, we have proposed a multi-rate Deep-Dilation Network (DDN) in V-Net for the segmentation of pancreas in CT-82 abdominal dataset. To overcome data-imbalance between bright and dark pixels, we propose a Weighted Fusion Loss (WFL) using Balanced Binary Cross-entropy (BBCE) loss and Smooth Dice Coefficient (SDC) loss. The proposed model attains a state-of-the-art performance for pancreas segmentation. The achieved dice score, sensitivity and precision are 83.31%, 87.70% and 97.07% respectively.

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10.1109/spin48934.2020.9071339

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Normalized Label Distribution: Towards Learning Calibrated, Adaptable and Efficient Activation Maps

arXiv (Cornell University) (2020)

Utkarsh Uppal Bharat Giddwani

The vulnerability of models to data aberrations and adversarial attacks influences their ability to demarcate distinct class boundaries efficiently. The network's confidence and uncertainty play a pivotal role in weight adjustments and the extent of acknowledging such attacks. In this paper, we address the trade-off between the accuracy and calibration potential of a classification network. We study the significance of ground-truth distribution changes on the performance and generalizability of various state-of-the-art networks and compare the proposed method's response to unanticipated attacks. Furthermore, we demonstrate the role of label-smoothing regularization and normalization in yielding better generalizability and calibrated probability distribution by proposing normalized soft labels to enhance the calibration of feature maps. Subsequently, we substantiate our inference by translating conventional convolutions to padding based partial convolution to establish the tangible impact of corrections in reinforcing the performance and convergence rate. We graphically elucidate the implication of such variations with the critical purpose of corroborating the reliability and reproducibility for multiple datasets.

10.48550/arxiv.2012.06876

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2020 CATARACTS Semantic Segmentation Challenge

arXiv (Cornell University) (2021)

Imanol Luengo Maria Grammatikopoulou Rahim Mohammadi Chris A. Walsh Chinedu Innocent Nwoye

10.48550/arxiv.2110.10965

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Eigen Vectors based Rotation Invariant Multi-Object Deep Detector

Bharat Giddwani Dheeraj Varma Mohana Murali Rama Krishna Gorthi

In this paper, we propose an accurate, scalable data-free approach based on eigenvectors and Convolutional Neural Networks (CNNs) for rotated object detection. Detecting an arbitrarily diverted object poses a challenging problem, as features extracted by CNNs are variant to small changes in shift and scale. They lack in performance for images at orientation different from input data. Hence, we introduce a novel two-step architecture, which detects multiple objects at any angle in an image efficiently. We utilize eigenvector analysis on the input image based on bright pixel distribution. The vertical and horizontal vectors are used as a reference to detect the deviation of an image from the original orientation. This analysis gives four orientations of the input image which, that pass through a pre-trained YOLOv3 with proposed decision criteria. Our approach referred to as "Eigen Vectors based Rotation Invariant Multi-Object Deep Detector" (EVRI-MODD), produces rotation invariant detection without any additional training on augmented data and also determines actual image orientation without any prior information. The proposed network achieves high performance on Pascal-VOC 2012 dataset. We evaluate our network performance on three differently rotated angles, 90°, 180°, and 270°, and achieves a significant gain in accuracy by 48%, 50%, and 47% respectively, over YOLOv3.

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10.1109/spin48934.2020.9070989

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