The Importance of Medical Multimedia

The Importance of
Medical Multimedia
Michael Riegler2, Pål Halvorsen2, Bernd Münzer1, Klaus Schoeffmann1
1 Institute of Information Technology
Klagenfurt University, Austria
2 Simula Research Laboratory
Norway

• Introduction & Overview
• Multimedia Data in Medicine
• Characteristics of Endoscopic Video
• Different Fields and Communities
• Application 1: Post-Procedural Usage of Surgery Videos
• Domain-Specific Storage for long-term Archiving
• Medical Video Content Analysis
• Medical Video Interaction
• Application 2: Diagnostic Decision Support
• Knowledge Transfer
• Analysis
• Feedback
• Explainability and Trust
• Conclusions & Outlook
Agenda
ACM Multimedia 2018 Tutorial The Importance of Medical Multimedia 2

Introduction

Inspections and intervention produce many kinds of data
• Medical text
• OR reports, Patient records…
• Sensor signals
• ECG, EEG, vital signs
• Medical images (radiology)
• Ultrasound, x-ray
• CT, MRI, PET, …
• Medical video
• Screenings
• Surgery
Multimedia Data in Medicine
à Signal Processing
à Medical Imaging
à Robotics
à Multimedia
à Data Mining
„Human EEG without alpha-rhythm“ by Andrii Cherninskyi / CC BY-SA
„Pankreatitis“ by Hellerhoff/ CC BY-SA„Ultrasound“, Public Domain

• Traditional open surgery ?
• Minimally invasive interventions
• Reduced trauma for patient
• Inherently available video signal
• Microscopic surgery
Video Data Sources in Medicine
„Lobektomia“ by Wojciech Filipiak/ CC BY-SA
„Cataract Surgery“, Public Domain
„Laparoscopy“, Public Domain

• Strongly increasing usage of videos and images in daily routine
• Endoscopic imaging as gold standard
• Availability of cheap storage capacity
• Manifold use cases
• Real-time support during surgery
• Sophisticated documentation of surgeries
• Diagnosis support
à Strong demand for effective storage, content processing and visualization!
Motivation

Medical Endoscopy

• Minimally-invasive surgery (“keyhole surgery”) or screening
• Enabled by modern video technology
• Endoscope as the ’’Eye of the Surgeon’’
• Videos are captured for
• Documentation
• Retrospective analysis
• Teaching / Education
Medical Endoscopy
„Laparoscopy“, Public Domain

Diagnostic Endoscopy
• Diagnosis / Inspections
• Gastroenterology (colonoscopy, gastroscopy)
• Bronchoscopy
• Hysteroscopy
• …
• Flexible endoscope
• Natural orifices
• WCE (Wireless capsule endoscopy)
„Kussmaul Gastroscopy“, Public Domain
„Colonoscopy“, Public Domain
„Kolon transversum“ by J.Guntau / CC BY-SA

Therapeutic Endoscopy
• Therapy / Surgery
• Laparoscopy
• Cholecystectomy
• Gynecological surgery
• Urological surgery
• …
• Arthroscopy
• …
• Rigid endoscope
• Small incisions „Laparoscopy“ by BruceBlaus / CC BY
„Arthroscopy“, Public Domain

Endoscopic Video Examples

Domain-specific Characteristics & Challenges
• Full HD or 4K (even stereo 3D)
• Single shot recordings
• Up to multiple hours
• Homogenous color distribution
• Visually very similar content
• Circular content area
• Restricted motion
• Geometric distortion
• Specular reflections
• Occlusions
• Smoke
• Noise, motion blur, blood, flying particles

Literature Overview

Overview
Münzer, Bernd, Klaus Schoeffmann, and Laszlo Böszörmenyi. "Content-based processing and analysis of endoscopic images and videos: A survey." Multimedia Tools and Applications (2017): 1-40.

Pre-Processing
• Image Enhancement
• Contrast enhancement, color misalignment
correction…
• Camera calibration and distortion correction
• Specular reflection removal
• Comb structure removal & super resolution
• …
• Information Filtering
• Frame filtering
• Image segmentation
T. Stehle. Removal of specular reflections in endoscopic images. Acta
Polytechnica: Journal of Advanced Engineering, 46(4):32–36, 2006.
J. Barreto, J. Roquette, P. Sturm, and F. Fonseca. Automatic
Camera Calibration Applied to Medical Endoscopy. In 20th
British Machine Vision Conference (BMVC ’09), 2009.
B. Münzer, K. Schoeffmann, and L. Böszörmenyi. Relevance Segmentation of Laparoscopic Videos. In 2013 IEEE International Symposium on Multimedia (ISM), pages 84–91, Dec. 2013.
A. Chhatkuli, A. Bartoli, A. Malti, and T. Collins. Live image parsing in uterine laparoscopy. In IEEE International Symposium on Biomedical Imaging (ISBI), 2014.

Real-time Support at Intervention Time
Applications
§ Diagnosis support
§ Robot-assisted surgery
§ Context awareness
§ Augmented reality
“Robotic surgical system”, Public Domain
T. Collins, D. Pizarro, A. Bartoli, M. Canis, and N. Bourdel. Computer-Assisted Laparoscopic myomectomy by augmenting the uterus with pre-operative MRI data. In 2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pages 243–248, Sept. 2014.
„Da Vinci Surgical System“ by Cmglee / CC BY-SA
Slightly modified from: M. P. Tjoa, S. M. Krishnan, et al. Feature extraction for the analysis of colon status from the endoscopic images. BioMedical Engineering OnLine, 2(9):1–17, 2003.

• 3D reconstruction
• Deforming tissue tracking
• Image registration
• Instrument detection and tracking
• Surgical workflow understanding
Enabling Techniques
L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov. Optical
techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery. Medical Image Analysis, 17(8):974–996, Dec. 2013.
S. Giannarou, M. Visentini-Scarzanella, and G. Z. Yang. Affine-invariant anisotropic detector for soft tissue tracking in minimally invasive
surgery. In Biomedical Imaging: From Nano to Macro, 2009. ISBI’09. IEEE International Symposium on, pages 1059–1062, 2009.

Post-Procedural Applications
Management and Retrieval
• Compression and storage
• Content-based retrieval
• Temporal video segmentation
• Video summarization
• Visualization & Interaction
Quality Assessment
§ Skills assessment
§ Education & Training
§ Error Rating
§ Assessment of intervention quality
M. Lux, O. Marques, K. Schöffmann, L. Böszörmenyi, and G. Lajtai. A novel tool for summarization of arthroscopic videos. Multimedia Tools and Applications, 46(2-3):521–544, Sept. 2009.
D. Liu, Y. Cao, W. Tavanapong, J. Wong, J. H. Oh, and P. C. de Groen. Quadrant coverage histogram: a new method
for measuring quality of colonoscopic procedures. In Engineering in Medicine and Biology Society, 2007. EMBS
2007. 29th Annual International Conference of the IEEE, pages 3470–3473, 2007.
J. Muthukudage, J. Oh, W. Tavanapong, J. Wong, and P. C. d. Groen. Color Based
Stool Region Detection in Colonoscopy Videos for Quality Measurements. In Y.-S. Ho,
editor, Advances in Image and Video Technology, number 7087 in Lecture Notes in
Computer Science, pages 61–72. Springer Berlin Heidelberg, Jan. 2012.

Post-Procedural Use of Surgery Videos

• Video documentation of endoscopic procedures is on the rise
• “a picture paints a thousand words“, a moving picture paints millions!
• In some countries even mandatory already
• Current documentation practice poses many problems:
• Hard task to retrieve relevant information
• Huge amounts of storage space
• High ratio of irrelevant data (“rubbish”)
• Very inefficient encoding (especially for HD content)
Motivation for Video Documentation

• Later inspection of specific moments
• Discussion of critical moments (e.g., with OP team)
• Information to patients
• Preparation of future interventions
• Forensics & investigations (e.g., comparisons)
• Training & teaching
• Surgical quality assessment (technical errors)
Use Cases

• Vision
• Archive and combine all relevant text, image, and video data
• Make it easily accessible
• Support surgeons at diagnosis, surgery planning, teaching, …
• Improve quality of interventions
• Challenges
• Isolated systems / separation of data
• Very Big Data
• Only small fraction is actually relevant
• Very specific domain characteristics
Towards a Medical Multimedia Information System

Full Storage of Endoscopic Videos
• Exemplary hospital
• 5 departments (Lap, Gyn, Arthro, GI, ENT)
• 2 operation rooms, each 4 ops/day, each op ca. 1-2h
• à i.e. 40 interventions per day, each ~ 90 mins.
• 60 hours video per day!
• Assumption: HD 1920x1080, H.264/AVC
• 270 GB / day (1h=4.5 GB)
• 1.9 TB / week
• 100 TB / year (200 TB MPEG-2)
4K: even more
Great challenge for a hospital’s IT department!

How to Reduce Storage Requirements?
Exploit domain-specific characteristics:
1. Spatial compression optimization
2. Temporal compression optimization
3. Perceptual quality based optimization
4. Long-term archiving strategy
Transcoding
up to 30%
up to 40%
up to 93%

Study on Video Quality
• Subjective quality assessment
• Catharina Hospital Eindhoven, NL
• 37 participants
• 19 experienced surgeons and 18 trainees
• 7 women, 30 men, average age: 40 years
• Subjective tests regarding
maximum compression
1) Perceivable quality loss
• Double-Stimulus (ITU-R BT.500-11)
• Switch between reference and test video
2) Perceivable semantic information loss
• Single Stimulus (ITU-R P.910)
• Assessing random videos (incl. reference)
Münzer, B., Schoeffmann, K., Böszörmenyi, L., Smulders, J. F., & Jakimowicz, J. J. (2014, May). Investigation of the impact of compression on the
perceptional quality of laparoscopic videos. In 2014 IEEE 27th International Symposium on Computer-Based Medical Systems (pp. 153-158). IEEE.
Session 1 Session 2

Assessment of Video Quality (Session 1)
-5
0
5
10
15
20
25
30
35
0
3000
6000
9000
12000
15000
18000
21000
24000
20 22 24 26 28 18 20 22 24 26 18 18
DifferenceMeanOpinionScore(DMOS)
Bitrate(Kb/s)
Test Conditions
Average bitrate Rating difference
1920x1080 1280x720 960x540 640x360
subjectively better
than reference
Reference video
(MPEG-2, HD, 20 (35) Mbit/s)
“lossless”
crf
(constant rate factor)

Assessment of Video Quality (Session 2)
1. Visually lossless with 8 Mbit/s Q1
(in comparison to 20 Mbit/s)
Reduction: 60% data vs. 0% MOS
2. Good quality with 2,5 Mbit/s and Q2
reduced resolution (1280x720)
3. Acceptable quality with 1,4 Mbit/s Q3
and lower resolution (640x360)
1
2
3

Example Videos
1280x720
Weak compression
16 MB
(crf 18)
640x360
Strong compression
0,8 MB
(crf 26)
20x

Long-term Performance
0
300
600
900
1200
1500
1800
2100
2400
0 1 2 3 4 5 6 7 8 9 10 11 12
Storagespace(GB)
Month
0
2500
5000
7500
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15000
17500
20000
22500
0 12 24 36 48 60 72 84 96 108 120
Storagespace(GB)
Month

Medical Video Content Analysis

1000 frames
(sampled from
17min with 1fps)
ACM Multimedia 2018 Tutorial
The Importance of Medical Multimedia
3
1

Content Relevance Filtering / Instrument Recognition
Münzer, B., Schoeffmann, K., & Böszörmenyi, L. (2013, December). Relevance segmentation of laparoscopic videos. In Multimedia (ISM), 2013 IEEE International Symposium on (pp. 84-91). IEEE.
Primus, M. J., Schoeffmann, K., & Böszörmenyi, L. (2015, June). Instrument classification in laparoscopic videos. In Content-Based Multimedia Indexing (CBMI), 2015 13th International Workshop on (pp. 1-6). IEEE.
Instrument detection for content understanding
(e.g., op phase segmentation, following
instruments in robot-assisted surgery)
Out-of-patient Scenes Blurry Scenes Border Area

Phase Segmentation (Cholecystectomy)
Manfred J. Primus, Klaus Schoeffmann and Laszlo Böszörmenyi. “Temporal Segmentation of Laparoscopic Videos into Surgical Phases“, in
Proceedings of the 14th International Workshop on Content-Based Multimedia Indexing (CBMI 2016), Bucharest, Romania, 2016
à Phase segmentation through instrument recognition
(color analysis, image moments, rules/heuristics)

Instrument Recognition/Tracking

Classification of OP Scene (Cataract Surgeries)
Manfred J. Primus, Doris Putzgruber-Adamitsch, Mario Taschwer, Bernd Münzer, Yosuf El-Shabrawi, Laszlo Böszörmenyi, and Klaus Schoeffmann. 2018. Frame-Based Classification of Operation Phases in Cataract Surgery
Videos. In Proceedings of the 24th International Conference on Multimedia Modeling 2018 (MMM2018). Lecture Notes in Computer Science, vol 10704, Springer, Cham, 241-253.

Surgical Action Classification

Gynecologic Laparoscopy: Relevant Surgical Actions
Dissection Coagulation Cutting cold
Cutting Hysterectomy Injection
SuturingSuction & Irrigation
Petscharnig, S., & Schöffmann, K. (2017). Learning laparoscopic video shot classification for gynecological surgery. Multimedia Tools and Applications, 1-19.

Gynecologic Laparoscopy: Relevant Surgical Actions
Dissection– 58 Segs / 35.517 Pics Coagulation– 212 Segs / 84.786 Pics Cutting cold – 271 Segs / 26.388 Pics
Cutting– 106 Segs / 92.653 Pics Hysterectomy– 25 Segs / 68.466 Pics Injection– 52 Segs / 52.355 Pics
Suturing– 92 Segs / 321.851 PicsSuction & Irrigation – 173 Segs /
73.977 Pics
1.105 segments (823.000 frames)
9h annotated video of 111 interventions
10-fold cross-validation

Deep Learning Surgical Actions
R...Recall P...Precision

• Early fusion
• Integrate motion information from consecutive frames
• Fded into CNN as additional input channel(s)
• Compare two approaches
• Block-Based Motion Estimation (BBME): using block matching
• Residual Motion (ResM): local motion
Fusing Temporal Information with CNNs

Early Fusion
HD
video
CNN
input
Stefan Petscharnig, Klaus Schöffmann, Jenny Benois-Pineau, Souad Chaabouni and Jörg Keckstein. 2018. Early and Late Fusion of Temporal Information for Classification of Surgical Actions in Laparoscopic Gynecology. In
Proceedings of the 31st IEEE CBMS International Symposium on Computer-Based Medical Systems. IEEE, Los Alamitos, CA, USA, 6 pages, 369-374.

Early Fusion - BBME
Create blocks of SxS pixels (e.g., 16x16)
Extract motion vectors (dx, dy) from blocks and smooth them
(d’x, d’y)
HD
video
CNN
input
Mean shift and normalization to [0..255]
! = max min
!(
2*
∗ 128 + 128, 255 , 0
(R,G,B)

Early Fusion – Residual Motion
compute length (E) and
normalize it with maximum
Estimate global motion for each pixel.
Find perspective transform (global motion) from frame i-1 to i
(using RANSAC) and subtract it à local/residual motion remains
(E)
HD
video
CNN
input
(R,G,B)

Early Fusion Results
RGB
BBME
as YCbCr
Cblue=vertical
Cred=horizontal
ResM

• Preliminary study showed that residual motion works much better when
using full-resolution HD videos as input
Early Fusion – Residual Motion

• Early fusion
• Integrate motion information from consecutive frames
• Feed into CNN as additional input channel(s)
• Compare two approaches
• Block-Based Motion Estimation (BBME): using block matching
• Residual Motion (ResM): local motion
• Late fusion
• Assume we already know scene boundaries and classify all frames of segments
• Temporal aggregation of single-frame classifications
• Majority vote (maximum occurrence of class in frames of scene)
• Average confidence
Fusing Temporal Information with CNNs

Evaluation Results – Early Fusion
worse
better
slightly better
better
baseline

Evaluation Results – Late Fusion
clearly better
clearly better
baseline

Evaluation Results – Early + Late Fusion
best result
baseline

• Fusion of temporal information with common CNNs can clearly improve
performance of surgical action classification (in Gynecologic Laparoscopy)
• Observations
• Additional temporal information clearly improves classification performance
• Early fusion
• BBME does not realy help
• ResM improves performance (5% and 9% boost of Rec and Prec – GoogLeNet)
• Late fusion
• Works well with both tested nets (13% and 25% boost of Rec and Prec – GoogLeNet; AlexNet even more)
• Averaging scheme outperforms majority vote
• Combination of early & late fusion achieves best result
• 17% and 33% boost of Rec and Prec for GoogLeNet
• Further work
• Evaluate with additional/deeper CNN architectures (deeper InceptionNet, ResNet)
• Evaluate patch-based approach
Surgical Action Classification – Summary

Deep Learning Surgical Actions
Confidence
Thresholdslow high
Stefan Petscharnig and Klaus Schoeffmann. 2018. ActionVis: An Explorative Tool to Visualize Surgical Actions in Gynecologic Laparoscopy. In Proceedings of the
24th International Conference on Multimedia Modeling 2018 (MMM2018). Lecture Notes in Computer Science, vol 10705, Springer, Cham, 348-351.

Medical Videos Dataset

LapGyn4: 4-part Laparoscopic Gynecology Dataset
Surgical Actions (~31K images) Anatomical Structures (~3K images)
Andreas Leibetseder, Stefan Petscharnig, Manfred Jürgen Primus, Sabrina Kletz, Bernd Münzer, Klaus Schoeffmann, and Jörg Keckstein. 2018. Lapgyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic
gynecology. In Proceedings of the 9th ACM Multimedia Systems Conference (MMSys '18). ACM, New York, NY, USA, 357-362.
Instrument Count (~22K images) Suturing on Anatomy (~1K images)
• Over 57,000 images
• 500+ surgeries
• Baseline Evaluations: GoogleNet
• 5-fold cross validation over 100 epochs

LapGyn4: Surgical Actions
• Why surgical actions?
• Key points in surgery
• Relevant for post-surgical analyses
• Classification (WAvg.)
• Very high performance
• 97% accuracy
• 92% recall
• Best in recognizing suturing
31,000+ images

LapGyn4: Anatomical Structures
3,000+ images
• Why anatomical structures?
• Main subjects to treatments
• Relevant for post surgical
analyses/AR tracking
• Very high performance
• 95% accuracy
• 91% recall
• Best results across metrics
• min. value: 0.8

LapGyn4: Instrument Count
22,000+ images
• Why counting instruments?
• Indicates action/inspection
• Facilitates identifying surgical
phases
• Good performance
• 92% accuracy
• 84% recall
• Best in recognizing zero
instruments

LapGyn4: Suturing on Anatomy
1,000+ images
• Why actions on anatomy?
• Key points in surgery
• Likely relevant in post-surgical
analyses
• Comparatively poor performance
• 80% accuracy
• 62% recall
• Visual context to similar?
• Not enough samples

Medical Video
Interaction Tools

Past/Current Status
Patient
names
File Explorers &
Segments to Download
2014
2009

Desired Status
Bernd Münzer, Klaus Schoeffmann and Laszlo Boeszoermenyi. “EndoXplore: A Web-based Video Explorer for Endoscopic Videos“. Proceedings of the IEEE International Symposium on Multimedia 2017 (ISM 2017), Taipei, Taiwan, 2017, pp. 1-2

Special Content Visualization

• Clinicians check full video recordings for occurrence of technical errors:
• Errors are rated according to standardized schemes (e.g., OSATS, GERT)
and surgeons are made aware of them
• Studies have shown that this significantly improves surgical quality
Surgical Quality Assessment (SQA)

Surgical Quality Assessment (SQA)

Surgical Quality Assessment (SQA) Software
• Integrating rating features
• More efficient video navigation/browsing
Marco A. Hudelist, Heinrich Husslein, Bernd Muenzer, Sabrina Kletz and Klaus Schoeffmann. “A Tool to Support Surgical Quality Assessment“,
in Proceedings of the Third IEEE International Conference on Multimedia Big Data (BigMM), Laguna Hills, CA, USA, 2017, pp. 238-239.

Diagnostic Decision Support

Challenges and Requirements

There is a Need for Complete Systems!
Medical knowledge transfer
Automated
analysis / detection / classification
Feedback / visualization
&
administrative

• Medical knowledge transfers – need DATA w/Ground Truth
• High detection accuracy
• Fast and efficient: real-time feedback and large scale
• Fit the normal examination procedures
• Assist administrative and report writing work
• Adhere to ethical, legal, privacy challenges & regulations
Key Challenges & Requirements
Multimedia
Medicine

Gastrointestinal (GI) Case Study
(challenges, system support, datasets, diagnostic decision support, ...)

• Many types of diseases can potentially affect the human gastrointestinal (GI) tract – the digestive system
• about 2.8 millions of new luminal GI cancers (esophagus, stomach, colorectal) are detected yearly
• the mortality is about 65%
• Screening of the GI tract using different types of endoscopy…
• is costly (colonoscopy according to NY Times: $1100/patient, $10 billion dollars)
• consumes valuable medical personnel time (1-2 hours)
• does not scale to large populations
• is intrusive to the patient
• …
• Current technology may potentially enable automatic algorithmic screening and assisted examinations
à a true interdisciplinary activity with high chances of societal impact
GI Tract Challenges and Potential

Colorectal Cancer
Women
Men
Colorectal cancer is the third most common cause of cancer
mortality for both women and men, and it is a condition
where early detection is important for survival,
i.e., a 5-year survival probability of
going from a low 10-30% if detected in later stages
to a high 90% survival probability in early stages.
Colonoscopy is not the ideal screening test.
Related to the cancer example, on average
20% of polyps (possible predecessors of cancer) are missed
or incompletely removed. The risk of getting cancer largely
depend on the endoscopists ability to detect and remove polyps.
A 1% increase in detection can decrease the risk of cancer with 3%.
The EU recommends screening of
everyone above 50 years old.
For example, Norway is now implementing
a national screening program

• A polyp is an abnormal growth
of tissue attached to the underlying mucosa
• Detection accuracy depends on experience and skills
• average miss rates of approx. 20%
• large inter- and intra-variations (e.g., a norwegian study shows variations between 36-65% for polyps)
• should reach a high (>85%) accuracy threshold to be acceptable
• Current technology may potentially enable
automated algorithmic assisted examinations
• Introduce a digital “third eye”
(with high accuracy and real-time processing)
Standard endoscopy: Live Polyp Detection

Video Capsule (PillCam)
§ Standard colonoscopy:
§ expensive
§ does not scale
§ intrusive

Video Capsule (PillCam)
§ Standard colonoscopy:
§ expensive
§ does not scale
§ intrusive
§ Wireless Video Capsule endoscopy:
§ better scale
§ less intrusive
§ possible to combine examinations!?
§ watch hours of video
§ less expensive?
(detection might lead to an endoscopy)

A complete System

System Overview

Medical Knowledge Transfer
(Data Collection)

• Need more data and therefore tools to efficiently annotate and tag data
Available GI Datasets
Name Contain Annotation Size Type Usage
CVC-ClinicDB Polyps GT masks 12000 images (several versions) Trad. ©, by permission
ETIS-Larib Polyp DB Polyps, Normal GT masks 1500 images Trad. ©, by permission
ASU-Mayo Clinic DB Polyps, Normal GT masks 20 videos Trad. ©, by permission
Colonoscopy Videos DB Various Lesions Sorted 76 videos Trad. Academic
Capsule Endoscopy DB Various Lesions and Findings Sorted 3170 images, 47 videos VCE Academic, by request
GastroAtlas Various Lesions and Findings Sorted, Text annotations 4449 videos Trad. Academic
WEO Atlas Various Lesions and Findings Sorted, Text annotations ? Trad. Academic
GASTROLAB Various Lesions and Findings Sorted, Text annotations ? Trad. Academic
Atlas of GE Various Lesions Sorted, Text annotations 669 images Trad. ©, by permission
KID Various Lesions Sorted 2500 + 47 videos Trad. ©, by permission
ASU-Mayo dataset: POLYPS
• 20 videos
• 10 with polyps, 10 without
• 8-64 seconds long
• varying resolution
• ~18.000 frames/images
• image mask of polyp (ground truth)
• (currently) restricted use
• Datasets are rare
• Often small amount of data
• Often missing annotation
• Few are truly publicly available and accessible
• Quality/resolution is bad
• Imbalanced
• Not enough “Normality”
• …
CVC: POLYPS
• CVC-356 – 356 polyp images, 1350 normal frames
• CVC-12K – 10025 polyp images, 1929 normal frames
• image mask of polyp (ground truth)
• (currently) restricted use

• Which image is not from the same class?
… and it gets worse …
• Making a mistake between cats and dogs may not matter,
but a misclassification here may have lethal consequences
Why Can’t CS People Do the Annotation!?
PylorusZ-line Z-line Z-line Z-line Z-line

Available time of the clinicians?

• Simple and efficient
• Web-based
• Assisted object tracking
Video Annotation Subsystem
"Expert Driven Semi-Supervised Elucidation Tool for Medical Endoscopic Videos"
Zeno Albisser, et. al.
Proceedings of MMSys, Portland, OR, USA, March 2015

• For large collection of images
• VV / Kvasir dataset
• Fully cleaned
• Feature extraction
mechanisms
• Different unsupervised
clustering algorithms
• Hierarchical image collection
visualization
• Open source: ClusterTag
https://bitbucket.org/mpg_projects/clustertag
ClusterTag: Image Clustering and Tagging Tool
"ClusterTag: Interactive Visualization, Clustering and Tagging Tool for Big Image Collections"
Konstantin Pogorelov, et. al.
Proceedings of ICMR, Bucharest, Romania, June 2017

• Multi-Class Image Dataset for Computer Aided GI Disease Detection
• GI endoscopy images
• Some images contain the position and configuration of the endoscope (scope guide)
• 8 different anomalies and anatomical landmarks
• v1: 500 images per class, 6 pre-extracted global features
• v2: 1000 images per class
• New information added in the future: http://datasets.simula.no/kvasir/
The Kvasir Dataset
"Kvasir: A Multi-Class Image-Dataset for Computer Aided Gastrointestinal Disease Detection"
Konstantin Pogorelov, et al.
Proceedings of MMSYS, Taiwan, June 2017

• Bowel Preparation Quality Video
• 21 GI endoscopy videos of colon
• Some frames contain the position and
configuration of the endoscope (scope
guide)
• 4 classes showing four-score BBPS-
defined bowel-preparation quality
• 0 - very dirty
• …
• 3 - very clean
• http://datasets.simula.no/nerthus/
The Nerthus Dataset
"Nerthus: A Bowel Preparation Quality Video Dataset"
Konstantin Pogorelov, et al.
Proceedings of MMSYS, Taiwan, June 2017

• Still need even more efficient tools and data of entire procedures
1. “Annotation” during examination
2. Video with bookmarks
3. Annotate bookmarks
4. Automatically annotate
neighboring frames using
object tracking – and verify
Next version of the annotation tool

GI Anomaly
Detection System

• Common approaches
• Handcrafted features
• Convolutional neural network
• Generative Adversarial Networks
• Easy to extend with new diseases
• Easy to extend with new algorithms
• Easy to train
• Results are explainable?
• Disease Localization?
• Real-time?
Requirements Detection and Automatic Analysis subsystem

Performance
(accuracy and speed)

§ Mayo dataset (18781 images/frames)
§ masks for all polyps
• GF:
• recall 98.50%, precision 93.88%, fps ~300
• CNN:
• Modified Inception v3: recall 95.86%, precision 80.78%, fps: ~30
• Inception v3 + WEKA: recall: 88.87%, precision: 89.16%, fps: ~30
ASU Mayo Dataset: Polyp Detection
”EIR - Efficient Computer Aided Diagnosis Framework for Gastrointestinal Endoscopies"
Michael Riegler, et. al.
Proceedings of CBMI, Bucharest, Romania, June 2016

• Resource consumption and processing performance of GF:
• Neural networks (also including GPU support)?
• tests so far: ~30 fps (same GPU as above)
• but adding layers, more networks, … !?? (newer GPU)
• Inception v3 TFL: 66 fps, plain CNN: ~40-45 fps
• GAN: ~12 fps (for 160x160)
ASU Mayo Dataset: Polyp Detection

• Process only frames containing polyps
• Performs image enhancement
• Detects curve-shaped objects and
local maximums
• Builds energy map and selects
4 possible locations
• Localization performance:
• recall 31.83 %,
• precision 32.07%
• ~30 fps
• later better GPU: ~75 fps (detection: 300 fps ; localization 100 fps)
ASU Mayo Dataset: First Try for Polyp Localization

• Vestre Viken (VV) multi-disease dataset (250 images per class)
• GF:
• recall 90.60 %
• precision 91.40%
• fps ~30
• CNN:
• recall: 87.20%
• precision: 87.90%
• fps: ~30
VV Dataset: Multi-Disease Detection
""Efficient disease detection in gastrointestinal videos - global features versus neural networks"
Multimedia Tools and Applications, 2017

• GF
• CNN
VV Dataset: Multi-Disease Detection
""Efficient disease detection in gastrointestinal videos - global features versus neural networks"
Multimedia Tools and Applications, 2017

• 7 different algorithms
• Convolutional neural networks (CNN) (2) – trained from scratch
• 3-layers
• 6-layers
• Transfer learning (1) – retrained Inception v3
• Global features (4)
• 2 global features (JCD, Tamura)
• 6 global features (JCD, Tamura, Color Layout, Edge Histogram, Auto Color Correlogram and PHOG)
• 2 different algorithms (Random forest and logistic model tree)
• 2 baselines
• Random Forrest with one global feature
• Majority class
• 2-folded cross validation
Kvasir Dataset v1: Multi-Disease Detection

DyedandLiftedPolypDyedResectionMargin

CecumPylorus

• Using same GF and some new deep features, i.e.,
• Pre-trained ImageNet dataset Inception v3
• ResNet50 models
• Used different ML classifications;
• random tree (RT)
• random forest (RF)
• logistic model tree (LMR) – performed best
• Uses weights of 1000 pre-defined concepts as
features
• Top layer input as features vector
(16384 for Inception v3 and 2048 for ResNet50)
Kvasir Dataset v1 à v2: Multi-Disease Detection
Pretrained
model
Output or top-
layer input
weights
WEKA for
classification
Team Approaches F1 FPS
SCL-UMD Global-features and deep-features extraction,
Inception-V3 and VGGNet CNN models, followed by
machine-learning-based classification using RT, RF, SVM
and LMR classifiers
0.848 1.3
FAST-NU-DS Global and local features combined followed by data size
reduction by applying K-means clustering and than
using logistic regression model for the classification
0.767 2.3
ITEC-AAU Two different custom Inception-like CNN models 0.755 1.4
HKBU A manifold learning method (bidirectional marginal
Fisher analysis) learning a compact representation of the
data, then machine-learning-based multi-class support
vector machine is used for the classification
0.703 2.2
SIMULA GF-features extraction, ResNet50 and Inception-V3 CNN
models and followed by machine-learning-based
classification using RT, RF and LMR classifiers
0.826 46.0

• 16 classes of anomalies and landmarks
• Very varying dataset sizes for the different classes
• Combination of retrained networks
Kvasir Dataset v2 à v3: Multi-Disease Detection

Compared:
• Handcrafted global features (GF-D) sing LIRE
• Retrained and fine tuned existing DL architectures (RT-D)
• Generative adversarial network (GAN)
• Combined various datasets captured by different equipment in
different hospitals.
• With our best working GAN-based approach, we reached
detection specificity of 94% and accuracy of 90.9% with only 356
training and 6,000 test samples
• The localization specificity and accuracy for the same training set
are 98.4% and 94.6% respectively.
The Next Level: Comparing Handcrafted and Deep
Learning Features – Cross Datasets

• 7 different algorithms
• Convolutional neural networks (CNN) (2) – trained from scratch
• 3-layers
• 6-layers
• Transfer learning (1) – retrained Inception v3
• Global features (4)
• 2 global features (JCD, Tamura)
• 6 global features
(JCD, Tamura, Color Layout, Edge Histogram, Auto Color Correlogram and PHOG)
• 2 different algorithms (Random forest and logistic model tree)
• 2 baselines
• Random Forrest with one global feature
• Majority class
• 2-folded cross validation
Nerthus Dataset: Bowel Cleanness Level

Preprocessing

• Too little data
• Blurry images due to camera motion
• Objects too close to camera
• Under or over scene lighting
• Flares
• Artificial objects and natural “contaminations”
• Low resolution of capsular endoscopes
• …
Data Challenges: Preprocessing

Data Enhancements for CNN Training

• Artifacts in the images can
influence the algorithm
• Understanding of what the
algorithm reacts to is crucial
Borders and Overlays

• Results on Kvasir + CVC-986
• Accuracy improved for
almost all models with some
preprocessing
Borders and Overlays

• Replacing artifacts in the
video/image
• Different methods
• From simple to more advanced
• Some difference but marginal
• Future: Does it have to look
good?
• Support the algorithm not the
human perception
• Different from usual GAN use
GAN inpainting of Navigation Box

Automatic Detection of Angiectasia
Video Capsule Endoscopy

• Angiectasia is a vascular lesions that can cause of
GI bleedings
• Medical specialists reach a detection accuracy of about 69%
• Medical systems should reach an 85% threshold to be
acceptable in clinical use
Angiectasia Detection

Angiectasia Detection: Varying Difficulty

• By far, GANs give the best
detection:
• sensitivity: 98%
• specificity: 100%
• BUT, sloooooow…
• Several approaches are better
than the average doctor (69%)
• Most of the approaches have a too
low detection rate, but still better
than the baseline
• Compromise between
accuracy and speed
Detection Compared

Detection Feedback

Detection Subsystem Outputs
• Visualize the output of the system to the medical doctors
• Simple and easy to understand (most important)
• Easy to integrate in hospitals
• Live support
• Useable for automatic reports, etc.

• Polyps
• Input:
Camera or Video files
• Output:
Live stream and
Performance reports
• Full HD
• Real-time: 30 FPS
Real-time Detection Feedback

Increasing Understanding
&
Assisting Administrative Work

Reporting of Endoscopies
Importance of Endoscopy Reporting
• Critical in communication between patients and
healthcare providers.
• Sometimes only evidence of the performed
procedure.
• Important in measuring the quality of endoscopies.
Current State Considered Poor
• Inconsistent descriptions of abnormalities
• Poor adoption of existing standards
• Time consuming (up to 15 minutes or more)
• Boring and lessens job satisfaction

Mimir: Reporting of Endoscopies
Goals of Mimir
• Give an easy to understand way of interpreting the
output of a neural network.
• Allows for deeper analysis of why the model produces a
given result.
• Class discriminatory visualizations based on selected class
and layer.
• Tools for uploading and managing various models.
• Provide a tool for the automatic generation of
modifiable medical reports.
• Produced Visualizations
• Visualizations produced using the grad-CAM technique.
• Takes key attributes from saliency and class activation maps.

Goals of Mimir
given result.
and layer.

So, all problems solved!!??

• Improve detection, localization and system performance
(retrieval, machine learning, features, search, real-time, distributed computing, scale, visualization, neural networks, user
interaction, object tracking, …)
1. Exploiting domain expert knowledge – build datasets
2. Integration of various data, multi-modality – new sensors
3. Explainable AI
4. Automated report system
5. Full system integration
6. Patient context information
7. Visualization, decision support
8. Integration of data from various sources / systems
9. Other areas in medicine
10. …
Many more…
Many Open Challenges…
"Multimedia and Medicine: Teammates for Better Disease Detection and Survival"
Michael Riegler, et. al.
Proceedings ACM MM, Amsterdam, The Netherlands, October 2016

• We have given several case-specific examples, but in general, they are common for MMIS
• Doctors want to use all the data for general support:
analysis, diagnostics, reporting, teaching, statistics, similarity search / comparisons, …
• Currently, …
• more and more high quality data is recorded / produced
• data analysis methods are (only) promising
• multi modal data analysis is not very common
• good visualization tools exist, but not used (e.g., AR, VR, …)
• some tools are missing
• many (other) areas produce separate (isolated) methods
• …
• but, we need a complete integrated system!
Ø Our multimedia community is needed
Summary

The End…
Multimedia
Medicine

The Importance of Medical Multimedia

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