# ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos

arXiv: https://arxiv.org/abs/2109.12448 · HTML: https://mlsanity.com/paper/2109.12448/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 25 Sep 2021.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks](https://mlsanity.com/paper/1808.08127/) (2018)
- [Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis](https://mlsanity.com/paper/1907.06099/) (2019)
- [RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments](https://mlsanity.com/paper/1909.10360/) (2019)
- [CaDIS: Cataract Dataset for Image Segmentation](https://mlsanity.com/paper/1906.11586/) (2019)
- [Relevance Detection in Cataract Surgery Videos by Spatio-Temporal Action Localization](https://mlsanity.com/paper/2104.14280/) (2021)
- [CataNet: Predicting remaining cataract surgery duration](https://mlsanity.com/paper/2106.11048/) (2021)
- [LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos](https://mlsanity.com/paper/2107.00875/) (2021)
- [Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation](https://mlsanity.com/paper/1903.11834/) (2019)
- [BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation](https://mlsanity.com/paper/2001.07093/) (2020)
- [Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks](https://mlsanity.com/paper/2006.11193/) (2020)
- [Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images](https://mlsanity.com/paper/2107.01502/) (2021)

## Built on by (most cited first, 8 of 8)

- [Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training](https://mlsanity.com/paper/2307.16660/) (2023)
- [Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection](https://mlsanity.com/paper/2312.06295/) (2023)
- [SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge](https://mlsanity.com/paper/2407.11906/) (2024)
- [GynSurg: A Comprehensive Gynecology Laparoscopic Surgery Dataset](https://mlsanity.com/paper/2506.11356/) (2025)
- [SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data](https://mlsanity.com/paper/2412.16078/) (2024)
- [Predicting Postoperative Intraocular Lens Dislocation in Cataract Surgery via Deep Learning](https://mlsanity.com/paper/2312.03401/) (2023)
- [WetCat: Enabling Automated Skill Assessment in Wet-Lab Cataract Surgery Videos](https://mlsanity.com/paper/2506.08896/) (2025)
- [CataractSAM-2: A Domain-Adapted Model for Anterior Segment Surgery Segmentation and Scalable Ground-Truth Annotation](https://mlsanity.com/paper/2603.21566/) (2026)

## Abstract (arXiv)

Semantic segmentation in surgical videos is a prerequisite for a broad range of applications towards improving surgical outcomes and surgical video analysis. However, semantic segmentation in surgical videos involves many challenges. In particular, in cataract surgery, various features of the relevant objects such as blunt edges, color and context variation, reflection, transparency, and motion blur pose a challenge for semantic segmentation. In this paper, we propose a novel convolutional module termed as textitReCal module, which can calibrate the feature maps by employing region intra-and-inter-dependencies and channel-region cross-dependencies. This calibration strategy can effectively enhance semantic representation by correlating different representations of the same semantic label, considering a multi-angle local view centering around each pixel. Thus the proposed module can deal with distant visual characteristics of unique objects as well as cross-similarities in the visual characteristics of different objects. Moreover, we propose a novel network architecture based on the proposed module termed as ReCal-Net. Experimental results confirm the superiority of ReCal-Net compared to rival state-of-the-art approaches for all relevant objects in cataract surgery. Moreover, ablation studies reveal the effectiveness of the ReCal module in boosting semantic segmentation accuracy.