What are the common detection errors?

Most detection errors occur when the instrument rapidly moves into or out of the field of view, inducing significant motion blur,or when the instrument center is near the image border and little part-evidence can be extracted.

How many degrees of angle are the instrument errors for the two sequences?

In addition, the instrument angle errors for both sequences are on average (and standard deviation) 4.18 (3.9) and 5.31 (4.9) degrees, respectively.

What is the key to allowing the overall instrument pose to be estimated robustly?

The different instruments parts are key to allowingthe overall instrument pose to be estimated robustly as they provides coherence in the detection results.

What is the probability distribution of a class label?

The authors assume that Ot:t+δ is conditionally independent of Oj:j+δ , j = {mδ < t|m ∈ Z+} given the class label and assume P (Ot:t+δ|Y = y) = ∏C c=1 P (O c t:t+δ|Y = y), where Oct:t+δ is the response sum for class c.

What is the probability distribution of the class label?

In order to estimatethe conditional likelihoods P (Ot:t+δ|Y = y) the authors use a validation set as in [13], i.e. labeled data separate from the training data used to train the classifier F (x).

What is the main reason for the difficulty in detecting surgical instruments?

In large part, this detection task is challenging due to illumination changes, specular regions,motion blur and the instrument being partially occluded at times.

How many sampling rounds did the authors run?

In their experiments, the authors ran RANSAC with 500 sampling rounds and let inliers be points within 24 pixels of the model, i.e. the size of the evaluation patch.

What is the promising approach for in-vivo detection and tracking?

Among the many approaches proposed in the last twenty years [1,2,6,7,8,9], recent detection-based schemes that rely on building statistical classifiers to evaluate the presence of the instrument appear to be the most promising for in-vivo detection and tracking [4,5,10].

What is the entropy threshold for the a classifier?

Their early stopping algorithm for multiclass ensemble classifiers is summarized in Fig. 2 and has two user parameters: δ, how often the stopping criteria should be evaluated and γ, the entropy threshold which determines when the label uncertainty is sufficiently low.

Open AccessBook Chapter10.1007/978-3-319-10470-6_86

Fast Part-Based Classification for Instrument Detection in Minimally Invasive Surgery

Q: What are the contributions in "Fast part-based classification for instrument detection in minimally invasive surgery" ?

In this paper, the authors present a novel technique for detecting surgical instruments by constructing a robust and reliable instrument-part detector. While such detectors are typically slow to use, the authors introduce a novel early stopping scheme for multiclass ensemble classifiers which acts as a cascade and significantly reduces the computational requirements at test time, ultimately allowing it to run at framerate. The authors evaluate the effectiveness of their approach on instrument detection in retinal microsurgery and laparoscopic image sequences and demonstrate significant improvements in both accuracy and speed.

Q: How many stages are used to detect a pelvic instrument?

Using the early-stopping technique allows detection to be achieved at 16.6 fps, as roughly only 11 stages are computed on average per evaluation.

Q: How many images are in the training set?

Since sequences contain images without instruments in them, the authors set the training set such that it contains roughly 100 images in it.

Q: What was the main purpose of the RFs in the MIS?

In [4], RFs were also used to handle instrument-background classification, giving way to instrument segmentations and the 3D pose.

Q: What is the probability distribution of the class label?

In order to estimatethe conditional likelihoods P (Ot:t+δ|Y = y) the authors use a validation set as in [13], i.e. labeled data separate from the training data used to train the classifier F (x).

Q: What is the main reason for the difficulty in detecting surgical instruments?

In large part, this detection task is challenging due to illumination changes, specular regions,motion blur and the instrument being partially occluded at times.

Raphael Sznitman, +2 more

- 14 Sep 2014

- Vol. 17, pp 692-699

TL;DR: This paper introduces a novel early stopping scheme for multiclass ensemble classifiers which acts as a cascade and significantly reduces the computational requirements at test time, ultimately allowing it to run at framerate.

Abstract: Automatic visual detection of instruments in minimally invasive surgery (MIS) can significantly augment the procedure experience for operating clinicians. In this paper, we present a novel technique for detecting surgical instruments by constructing a robust and reliable instrument-part detector. While such detectors are typically slow to use, we introduce a novel early stopping scheme for multiclass ensemble classifiers which acts as a cascade and significantly reduces the computational requirements at test time, ultimately allowing it to run at framerate. We evaluate the effectiveness of our approach on instrument detection in retinal microsurgery and laparoscopic image sequences and demonstrate significant improvements in both accuracy and speed.

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Most frequently asked questions

1. What are the contributions in "Fast part-based classification for instrument detection in minimally invasive surgery" ?

In this paper, the authors present a novel technique for detecting surgical instruments by constructing a robust and reliable instrument-part detector.. While such detectors are typically slow to use, the authors introduce a novel early stopping scheme for multiclass ensemble classifiers which acts as a cascade and significantly reduces the computational requirements at test time, ultimately allowing it to run at framerate.. The authors evaluate the effectiveness of their approach on instrument detection in retinal microsurgery and laparoscopic image sequences and demonstrate significant improvements in both accuracy and speed.

2. How many stages are used to detect a pelvic instrument?

Using the early-stopping technique allows detection to be achieved at 16.6 fps, as roughly only 11 stages are computed on average per evaluation.

3. How many images are in the training set?

Since sequences contain images without instruments in them, the authors set the training set such that it contains roughly 100 images in it.

4. What was the main purpose of the RFs in the MIS?

In [4], RFs were also used to handle instrument-background classification, giving way to instrument segmentations and the 3D pose.

Fig. 1. (a) Example image with instrument-parts highlighted. (b) Examples of three image features overlayed on the evaluated image patch. (c) Plot of accumulated responses as a function of stage evaluations using a trained classifier. Each curve shows the estimate for a specific class. (d) Final response map using the classifier at each image location and scale with non-maximum suppression applied.

Fig. 3. Examples of retinal microsurgery instrument detections and depiction of the number of stage evaluations at corresponding image regions. For both tested sequences, we plot detection accuracy as function of the detection sensitivity threshold.

Fig. 2. Multiclass Early-Stopping Algorithm

Table 1. Spine and pelvic surgery image sequence statistics

Fig. 4. Visual results on pelvic (rows 1 and 2) and spine (row 3) surgery image sequences. Detected instrument center (green boxes) and orientation (extracted from the detected instrument shaft) overlayed onto the image (best viewed in color).

Citations

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- 01 Nov 2000

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TL;DR: This approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos, and applies a region proposal network (RPN) and a multimodal two stream convolutional network for object detection to jointly predict objectness and localization on a fusion of image and temporal motion cues.

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References

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The Elements of Statistical Learning

Trevor Hastie, +2 more

- 01 Jan 2001

29.4K

Journal Article•10.1198/TECH.2003.S770

The Elements of Statistical Learning

Eric R. Ziegel

- 01 Aug 2003

- Technometrics

TL;DR: Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.

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Journal Article•10.1023/B:VISI.0000013087.49260.FB

Robust Real-Time Face Detection

Paul A. Viola, +1 more

- 01 May 2004

- International Journal of Computer Vision

TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.

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Robust real-time face detection

Paul A. Viola, +1 more

- 07 Jul 2001

TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.

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Journal Article•10.1109/TBME.2012.2229278

Toward Detection and Localization of Instruments in Minimally Invasive Surgery

Max Allan, +5 more

- 01 Apr 2013

- IEEE Transactions on Biomedical Engineer...

TL;DR: A probabilistic supervised classification method to detect pixels in laparoscopic images that belong to surgical tools and uses the classifier output to initialize an energy minimization algorithm for estimating the pose of a prior 3-D model of the instrument within a level set framework.

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Fast Part-Based Classification for Instrument Detection in Minimally Invasive Surgery

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AI Agents for this Paper

Most frequently asked questions

1. What are the contributions in "Fast part-based classification for instrument detection in minimally invasive surgery" ?

2. How many stages are used to detect a pelvic instrument?

3. How many images are in the training set?

4. What was the main purpose of the RFs in the MIS?

5. What are the common detection errors?

6. How many degrees of angle are the instrument errors for the two sequences?

7. What is the key to allowing the overall instrument pose to be estimated robustly?

8. What is the probability distribution of a class label?

9. What is the probability distribution of the class label?

10. What is the main reason for the difficulty in detecting surgical instruments?

11. How many sampling rounds did the authors run?

12. What is the promising approach for in-vivo detection and tracking?

13. What is the entropy threshold for the a classifier?

Figures

Citations

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

Somite Development in Zebrafish

Vision-based and marker-less surgical tool detection and tracking: a review of the literature

Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection

References

The Elements of Statistical Learning

The Elements of Statistical Learning

Robust Real-Time Face Detection

Robust real-time face detection

Toward Detection and Localization of Instruments in Minimally Invasive Surgery

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EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

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Vision-based and marker-less surgical tool detection and tracking: a review of the literature