Instrument Tracking via Online Learning in Retinal Microsurgery

Li, Yeqing; Chen, Chen; Huang, Xiaolei; Huang, Junzhou

doi:10.1007/978-3-319-10404-1_58

Cited by 22 publications

(24 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From left to right: RM1, RM2, RM3, Lapa. Methods: POSE [27], ITOL [28], DDVT [26], Ours15 [15], Ours (black). Our tracker shows significant improvement over our previous tracking-bydetection method (green).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Surgical tool tracking by on-line selection of structural correlation filters

Węsierski

Jezierska

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-In visual tracking of surgical instruments, correlation filtering finds the best candidate with maximal correlation peak. However, most trackers only consider capturing target appearance but not target structure. In this paper we propose surgical instrument tracking approach that integrates prior knowledge related to rotation of both shaft and tool tips. To this end, we employ rigid parts mixtures model of an instrument. The rigidly composed parts encode diverse, pose-specific appearance mixtures of the tool. Tracking search space is confined to the neighbourhood of tool position, scale, and rotation with respect to previous best estimate such that the rotation constraint translates into querying subset of templates. Qualitative and quantitative evaluation on challenging benchmarks demonstrate state-of-theart results.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Evaluation metric We compare our tracker to state-of-theart methods in surgical tool tracking: POSE [27], ITOL [28], DDVT [26], and our previous method from [15]. To this end, we follow the standard evaluation metric of thresholded detections to evaluate the proposed method.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Surgical tool tracking by on-line selection of structural correlation filters

Węsierski

Jezierska

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

show abstract

“…Some other vision-based methods exploit the geometric constraints [8] and the gradientlike features [9,10], in order to identify the shaft of instrument, but fail to provide more accurate 3D positions of the instrument tip. Machine learning techniques [11][12][13][14][15][16][17][18][19] introduced into the instrument detection and tracking provide training of their discriminative classifiers/models according to the input visual features of the foreground (instrument tip or shaft). Edge pixel features [11] and fast corner features [12] are utilized to train the appearance models of surgical instrument based on the likelihood map.…”

Section: Introductionmentioning

confidence: 99%

“…Edge pixel features [11] and fast corner features [12] are utilized to train the appearance models of surgical instrument based on the likelihood map. To overcome the appearance changing of instrument, the online learning technique [13] was introduced into the tracking process. Some state-of-the-art image feature descriptors, e.g.…”

Section: Introductionmentioning

confidence: 99%

Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method

Zhao

Voros

Weng

et al. 2017

Computer Assisted Surgery

View full text Add to dashboard Cite

Background: Worldwide propagation of minimally invasive surgeries (MIS) is hindered by their drawback of indirect observation and manipulation, while monitoring of surgical instruments moving in the operated body required by surgeons is a challenging problem. Tracking of surgical instruments by vision-based methods is quite lucrative, due to its flexible implementation via software-based control with no need to modify instruments or surgical workflow. Methods: A MIS instrument is conventionally split into a shaft and end-effector portions, while a 2D/3D tracking-by-detection framework is proposed, which performs the shaft tracking followed by the end-effector one. The former portion is described by line features via the RANSAC scheme, while the latter is depicted by special image features based on deep learning through a well-trained convolutional neural network. Results: The method verification in 2D and 3D formulation is performed through the experiments on ex-vivo video sequences, while qualitative validation on in-vivo video sequences is obtained. Conclusion: The proposed method provides robust and accurate tracking, which is confirmed by the experimental results: its 3D performance in ex-vivo video sequences exceeds those of the available state-of -the-art methods. Moreover, the experiments on in-vivo sequences demonstrate that the proposed method can tackle the difficult condition of tracking with unknown camera parameters. Further refinements of the method will refer to the occlusion and multi-instrumental MIS applications.

show abstract

“…Among a variety of surgical tools in ophthalmic operations, the beveled needle is a widely used surgical instrument for delivering the drug into micro-structural anatomies of the eye such as retinal blood vessels and sub-retinal areas. Many studies have been carried out with significant progress in the needle segmentation through microscopic images [5][6][7][8]. These works achieved satisfactory results using either color-based or geometry-based features.…”

Section: Introductionmentioning

confidence: 99%

Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery

et al. 2017

View full text Add to dashboard Cite

Needle segmentation is a fundamental step for needle reconstruction and image-guided surgery. Although there has been success stories in needle segmentation for non-microsurgeries, the methods cannot be directly extended to ophthalmic surgery due to the challenges bounded to required spatial resolution. As the ophthalmic surgery is performed by finer and smaller surgical instruments in micro-structural anatomies, specifically in retinal domains, difficulties are raised for delicate operation and sensitive perception. To address these challenges, in this paper we investigate needle segmentation in ophthalmic operation on 60 Optical Coherence Tomography (OCT) cubes captured during needle injection surgeries on ex-vivo pig eyes. Furthermore, we developed two different approaches, a conventional method based on morphological features (MF) and a specifically designed full convolution neural networks (FCN) method, moreover, we evaluate them on the benchmark for needle segmentation in the volumetric OCT images. The experimental results show that FCN method has a better segmentation performance based on four evaluation metrics while MF method has a short inference time, which provides valuable reference for future works.

show abstract

Instrument Tracking via Online Learning in Retinal Microsurgery

Cited by 22 publications

References 16 publications

Surgical tool tracking by on-line selection of structural correlation filters

Surgical tool tracking by on-line selection of structural correlation filters

Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method

Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery

Contact Info

Product

Resources

About