# Depth-Driven Geometric Prompt Learning for Laparoscopic Liver Landmark Detection

arXiv: https://arxiv.org/abs/2406.17858 · HTML: https://mlsanity.com/paper/2406.17858/

## Key facts

- Cited by 9 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 Jun 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [Reverse Attention for Salient Object Detection](https://mlsanity.com/paper/1807.09940/) (2018)
- [How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images](https://mlsanity.com/paper/2306.13731/) (2023)
- [Structured Landmark Detection via Topology-Adapting Deep Graph Learning](https://mlsanity.com/paper/2004.08190/) (2020)
- [Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image](https://mlsanity.com/paper/2208.13241/) (2022)
- [OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers](https://mlsanity.com/paper/2207.02255/) (2022)
- [Ladder Fine-tuning approach for SAM integrating complementary network](https://mlsanity.com/paper/2306.12737/) (2023)
- [An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion](https://mlsanity.com/paper/2401.15753/) (2024)

## Built on by (most cited first, 9 of 9)

- [Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection](https://mlsanity.com/paper/2504.15152/) (2025)
- [Synergistic Bleeding Region and Point Detection in Laparoscopic Surgical Videos](https://mlsanity.com/paper/2503.22174/) (2025)
- [BCRNet: Enhancing Landmark Detection in Laparoscopic Liver Surgery via Bezier Curve Refinement](https://mlsanity.com/paper/2506.15279/) (2025)
- [Topology-Constrained Learning for Efficient Laparoscopic Liver Landmark Detection](https://mlsanity.com/paper/2507.00519/) (2025)
- [Subsampled Randomized Fourier GaLore for Adapting Foundation Models in Depth-Driven Liver Landmark Segmentation](https://mlsanity.com/paper/2511.03163/) (2025)
- [MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection](https://mlsanity.com/paper/2511.04255/) (2025)
- [Depth Augmented and FE Free 3D/2D Liver Registration for Laparoscopic Liver AR](https://mlsanity.com/paper/2602.17517/) (2026)
- [Benchmarking Endoscopic Surgical Image Restoration and Beyond](https://mlsanity.com/paper/2505.19161/) (2025)
- [Attenuation-Resilient Alternating Optimization for Laparoscopic Liver Landmark Detection](https://mlsanity.com/paper/2605.26630/) (2026)

## Abstract (arXiv)

Laparoscopic liver surgery poses a complex intraoperative dynamic environment for surgeons, where remains a significant challenge to distinguish critical or even hidden structures inside the liver. Liver anatomical landmarks, e.g., ridge and ligament, serve as important markers for 2D-3D alignment, which can significantly enhance the spatial perception of surgeons for precise surgery. To facilitate the detection of laparoscopic liver landmarks, we collect a novel dataset called L3D, which comprises 1,152 frames with elaborated landmark annotations from surgical videos of 39 patients across two medical sites. For benchmarking purposes, 12 mainstream detection methods are selected and comprehensively evaluated on L3D. Further, we propose a depth-driven geometric prompt learning network, namely D2GPLand. Specifically, we design a Depth-aware Prompt Embedding (DPE) module that is guided by self-supervised prompts and generates semantically relevant geometric information with the benefit of global depth cues extracted from SAM-based features. Additionally, a Semantic-specific Geometric Augmentation (SGA) scheme is introduced to efficiently merge RGB-D spatial and geometric information through reverse anatomic perception. The experimental results indicate that D2GPLand obtains state-of-the-art performance on L3D, with 63.52% DICE and 48.68% IoU scores. Together with 2D-3D fusion technology, our method can directly provide the surgeon with intuitive guidance information in laparoscopic scenarios.