Comparing ensemble methods combined with different aggregating models using micrograph cell segmentation as an initial application example.

Strategies such as ensemble learning and averaging techniques try to reduce the variance of single deep neural networks. The focus of this study is on ensemble averaging techniques, fusing the results of differently initialized and trained networks. Thereby, using micrograph cell segmentation as an application example, various ensembles have been initialized and formed during network training, whereby the following methods have been applied: (a) random seeds, (b) L 1-norm pruning, (c) variable numbers of training examples, and (d) a combination of the latter 2 items. Furthermore, different averaging methods are in common use and were evaluated in this study. As averaging methods, the mean, the median, and the location parameter of an alpha-stable distribution, fit to the histograms of class membership probabilities (CMPs), as well as a majority vote of the members of an ensemble were considered. The performance of these methods is demonstrated and evaluated on a micrograph cell segmentation use case, employing a common state-of-the art deep convolutional neural network (DCNN) architecture exploiting the principle of the common VGG-architecture. The study demonstrates that for this data set, the choice of the ensemble averaging method only has a marginal influence on the evaluation metrics (accuracy and Dice coefficient) used to measure the segmentation performance. Nevertheless, for practical applications, a simple and fast estimate of the mean of the distribution is highly competitive with respect to the most sophisticated representation of the CMP distributions by an alpha-stable distribution, and hence seems the most proper ensemble averaging method to be used for this application.

Number of necessary training examples for Neural Networks with different number of trainable parameters.

In this work, the network complexity should be reduced with a concomitant reduction in the number of necessary training examples. The focus thus was on the dependence of proper evaluation metrics on the number of adjustable parameters of the considered deep neural network. The used data set encompassed Hematoxylin and Eosin (H&E) colored cell images provided by various clinics. We used a deep convolutional neural network to get the relation between a model's complexity, its concomitant set of parameters, and the size of the training sample necessary to achieve a certain classification accuracy. The complexity of the deep neural networks was reduced by pruning a certain amount of filters in the network. As expected, the unpruned neural network showed best performance. The network with the highest number of trainable parameter achieved, within the estimated standard error of the optimized cross-entropy loss, best results up to 30% pruning. Strongly pruned networks are highly viable and the classification accuracy declines quickly with decreasing number of training patterns. However, up to a pruning ratio of 40%, we found a comparable performance of pruned and unpruned deep convolutional neural networks (DCNN) and densely connected convolutional networks (DCCN).

Review of free software tools for image analysis of fluorescence cell micrographs.

An increasing number of free software tools have been made available for the evaluation of fluorescence cell micrographs. The main users are biologists and related life scientists with no or little knowledge of image processing. In this review, we give an overview of available tools and guidelines about which tools the users should use to segment fluorescence micrographs. We selected 15 free tools and divided them into stand-alone, Matlab-based, ImageJ-based, free demo versions of commercial tools and data sharing tools. The review consists of two parts: First, we developed a criteria catalogue and rated the tools regarding structural requirements, functionality (flexibility, segmentation and image processing filters) and usability (documentation, data management, usability and visualization). Second, we performed an image processing case study with four representative fluorescence micrograph segmentation tasks with figure-ground and cell separation. The tools display a wide range of functionality and usability. In the image processing case study, we were able to perform figure-ground separation in all micrographs using mainly thresholding. Cell separation was not possible with most of the tools, because cell separation methods are provided only by a subset of the tools and are difficult to parametrize and to use. Most important is that the usability matches the functionality of a tool. To be usable, specialized tools with less functionality need to fulfill less usability criteria, whereas multipurpose tools need a well-structured menu and intuitive graphical user interface.

[Future of mammography-based imaging]. / Zukunft mammographiebasierter Bildgebung.

Mammography is the central diagnostic method for clinical diagnostics of breast cancer and the breast cancer screening program. In the clinical routine complementary methods, such as ultrasound, tomosynthesis and optional magnetic resonance imaging (MRI) are already combined for the diagnostic procedure. Future developments will utilize investigative procedures either as a hybrid (combination of several different imaging modalities in one instrument) or as a fusion method (the technical fusion of two or more of these methods) to implement fusion imaging into diagnostic algorithms. For screening there are reasonable hypotheses to aim for studies that individualize the diagnostic process within the screening procedure. Individual breast cancer risk prediction and individualized knowledge about sensitivity and specificity for certain diagnostic methods could be tested. The clinical implementation of these algorithms is not yet in sight.

Automatic texture-based analysis in ultrasound imaging of ovarian masses.

PURPOSE: To assess the diagnostic accuracy of a new automatic texture-based algorithm (ATBA) in ultrasound imaging of ovarian masses and to compare its performance to subjective assessment by examiners with different levels of ultrasound experience. MATERIALS AND METHODS: A total of 105 ultrasound images from three different groups of ovarian lesions (malignancies, functional cysts, and dermoid cysts) were evaluated using ATBA and by a total of 36 examiners with four different levels of experience (9 junior trainees, 8 senior trainees, 11 senior gynecologists, and 8 experts). Cohen's κ, Youden's indices, and the sensitivity and specificity of ATBA and of each observer were calculated for every subgroup of ovarian lesions. RESULTS: ATBA classified 78 of the 105 masses correctly (κ = 0.62) - results that were significantly better than those of the junior and senior trainees (p = 0.02 and p < 0.01), while differences from the group of level II examiners did not reach statistical significance (p = 0.27). The best diagnostic performance (κ = 0.70) was obtained by the group of expert level III ultrasonographers. The best classification rates overall, including both ATBA and subjective assessments, were achieved in the detection of functional cysts (Youden's indices from 0.73 to 0.85), while the poorest diagnostic performance was obtained for the classification of dermoid cysts (Youden's indices from 0.28 to 0.55). CONCLUSION: ATBA showed a significantly better diagnostic performance than observers with low or medium levels of experience, emphasizing its potential value for training purposes and in providing additional diagnostic assistance for inexperienced observers.

Computer-assisted diagnosis for precancerous lesions in the esophagus.

OBJECTIVES: The interpretation of endoscopic findings by gastroenterologists is still a difficult and highly subjective task. Despite important developments such as chromo-endoscopy, pit pattern analysis, fluorescence imaging as well as narrow band imaging it still requires lots of experience and training with a certain tentativeness until the final biopsy. By the development of computer-assisted diagnosis (CAD) systems this process can be supported. METHODS: This paper presents a new approach to CAD for precancerous lesions in the esophagus based on color-texture analysis in a content-based image retrieval (CBIR) framework. The novelty of our approach lies in the combination of newly developed color-texture features with the interactive feedback loop provided by a relevance feedback algorithm. This allows the expert to steer the query and is still robust against accidental false decisions. RESULTS: We reached an inter-rater reliability of kappa = 0.71 on a database of 390 endoscopic images. The retrieval accuracy didn't change significantly until a wrong decision rate of 20%. CONCLUSIONS: Thus, the system could be able to support practitioners with less experience or in private practice. In combination with a connected case database it can also support case-based reasoning for the diagnostic decision process.

The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process.

Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predict breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value < 0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z) = 0.89 +/- 0.01, the decision-tree approach in A(z) = 0.87 +/- 0.01, and the ANN approach in A(z) = 0.88 +/- 0.01.

[Experiences with 'optical' biopsies of leukoplakia of the vocal folds]. / Erfahrungen mit optischen Stimmlippenbiopsien bei Leukoplakien.

BACKGROUND: It is often difficult to distinguish leucoplakia from carcinoma or chronic laryngitis. In this study, we examined if color texture optical biopsies are able to detect leukoplakia reliably and to distinguish this from normal vocal fold tissue. METHODS: 25 images from patients with a normal larynx and 25 images from patients with leucoplakia were analysed retrospectively. The images were recorded with a rigid 90 degrees -laryngoscope (Wolf Typ 4450.571) during a clinical setting and were recorded by a S-VHS-videorecorder. With a software program (InSegT), regions with leucoplakia, normal tissue and suspicious tissue were manually marked. Within each marked region, the use of color texture analysis numeric features were calculated to characterize the surface in texture and color. Color histogramms (HST), Sum- and Difference histogramms (S/D), Statistical Geometric Features (SGF) and Grey-Value-Dependent-Matrix (GLDM) were used. PATIENTS: 29 women and 21 men (age 18 to 81 years, average of 53 years), who were examined in our clinic from 10/1999 - 8/2003, took part in this study. RESULTS: The automatized classification for color texture analysis resulted in 71 % for leucoplakia and 97 % for normal tissue. CONCLUSION: Optical biopsies can contribute to find the correct diagnosis. However, classification results must still get better when optical biopsies should be used in clinical practice.

A semantic approach to segmentation of overlapping objects.

OBJECTIVES: This paper aims at introducing a novel approach for segmentation of overlapping objects and at demonstrating its applicability to medical images. METHODS: This work details a novel approach enhancing the known theory of full-segmentation of an image into regions by lifting it to a semantic segmentation into objects. Our theory allows the formal description of partitioning an image into regions on the first level and allowing the occurrence of overlaps and occlusions of objects on a second, semantic level. Possible applications for the use of this 'semantical segmentation' are the analysis of radiographs and micrographs. We demonstrate our approach by the example of segmentation and separation of overlapping cervical cells and cell clusters on a set of 787 image pairs of registered PAP- and DAPI-stained micrographs. The semantical cell segmentation yielding areas of cell plasmas and nuclei are compared to a manual segmentation of the same images, where 2212 cells have been labeled. A direct comparison of over and under-segmentation between the two segmentation sets yields a mean difference value of 10.15% for the nuclei and 10.80% for the plasma. RESULTS: Using the proposed theory of semantical segmentation of images in combination with adequate models of the image contents, our approach allows identifying, separating and distinguishing several overlapping, occluding objects in medical images. Applying the proposed theory to the application of cervical cell segmentation from overlapping cell clusters and aggregates, it can be seen that it is possible to formally describe the complex image contents. CONCLUSIONS: The proposed method of semantical segmentation is a mighty tool and under the assumption of the subtractive transparency model can be used in different medical image processing applications such as radiology and microscopy. By using alternative models to solve the ambiguities attached to overlaps and occlusions, further fields of application can be addressed.

Establishing an international reference image database for research and development in medical image processing.

INTRODUCTION: The lack of comparability of evaluation results is one of the major obstacles of research and development in Medical Image Processing (MIP). The main reason for that is the usage of different image datasets with different quality, size and Gold standard. OBJECTIVES: Therefore, one of the goals of the Working Group on Medical Image Processing of the European Federation for Medical Informatics (EFMI WG MIP) is to develop first parts of a Reference Image Database. METHODS: Kernel of the concept is to identify highly relevant medical problems with significant potential for improvement by MIP, and then to provide respective reference datasets. The EFMI WG MIP has primarily the role of a specifying group and an information broker, while the provider user relationships are defined by bilateral co-operation or license agreements. RESULTS: An explorative database prototype has been implemented using the MySQL database software on the Web. Templates for provider user agreements have been worked out and already applied for own 'pre-RID-MIP' co-operations of the authors. DISCUSSION AND CONCLUSION: First steps towards a comprehensive reference image database have been done. Issues like funding, motivation, management, provision of Gold standards and evaluation guidelines are to be solved. Due to the interest from research groups and industry the efforts will be continued.

Functional imaging of vocal fold vibration: digital multislice high-speed kymography.

For the diagnosis of voice disorders, and especially for the classification of hoarseness, direct observation of vocal fold vibration is essential. Furthermore, a quantitative description of the movement of the vocal fold becomes increasingly necessary to document and compare findings as well as the progression of speech therapy. On the base of digital high-speed sequences of vocal fold vibration, multiple "functional images"-also called digital kymograms-are obtained using image- and signal-processing algorithms. Digital kymograms can serve as a powerful aid for visualization, description, and classification of vocal fold vibration and as an intermediate step for image interpretation by biomechanical modeling. This visualization technique will be discussed and compared to other techniques currently available: videokymography and videostroboscopy. The technique is applied to several clinical examples: aperiodic processes (phonation onset), irregular vocal fold vibration (paralysis of the recurrent nerve), particular vibration modes (anterior-posterior modes), and running speech.

High-speed digital imaging of neoglottic vibration after total laryngectomy.

OBJECTIVES: To establish the applicability of digital high-speed imaging in studying neoglottic mucosal vibration after total laryngectomy and to perform a structured evaluation of the recordings using a standardized assessment form to gain insight about the anatomical and morphologic characteristics of the neoglottis. DESIGN: Evaluation of a new clinical tool and description of clinical disorders in a patient survey. SETTING: The Netherlands Cancer Institute, Amsterdam. PATIENTS: Forty-six patients who underwent laryngectomy, 36 who underwent standard total laryngectomy and 10 who underwent a partial or total pharynx reconstruction (ie, myocutaneous pectoralis major flap [n = 4], free radial forearm flap [n = 2], tubed gastric pull-up [n = 3], and full gastric pull-up [n = 1]). INTERVENTION: Digital high-speed imaging, using a 90 degrees rigid laryngoscope, of the neoglottic vibration in prosthetic tracheoesophageal speakers after total laryngectomy. MAIN OUTCOME MEASURES: Digital high-speed imaging might overcome some of the problems of stroboscopy in studying irregular voices and could, therefore, be expected to give more insight into the anatomical and morphologic characteristics of the neoglottis. RESULTS: Digital high-speed recordings could be obtained in 44 of 46 patients. Using a structured evaluation form, a wide variability in anatomical and morphologic features could be established. CONCLUSIONS: Digital high-speed imaging appeared to be a useful tool in studying the irregular vibrations of the neoglottis. Evaluation by the structured evaluation form gives a good idea about the wide variability in anatomical and morphologic features of the neoglottis.

Imaging of vocal fold vibration by digital multi-plane kymography.

Digital multi-plane kymography is presented as a new method to demonstrate vocal fold vibration from digital high-speed recordings. Single lines from digital high-speed sequences of laryngoscopical examinations are concatenated to images, which are called kymograms. In order to reveal anterior-posterior (AP) modes of vibration several kymograms from different location of the glottis can be obtained from a single recording. Problems due to rotation of the endoscope or relative movements of patient or examiner can be solved by image processing algorithms specifically designed for this application. Different types of phonation onset and examples of voice disorders are given.

Phonation onset: vocal fold modeling and high-speed glottography.

Phonation onset is discussed in the framework of dynamical systems as a Hopf bifurcation, i.e., as a transition from damped to sustained vocal fold oscillations due to changes of parameters defining the underlying laryngeal configuration (e.g., adduction, subglottal pressure, muscular activity). An analytic envelope curve of the oscillation onset is deduced by analyzing the Hopf bifurcation in mathematical models of the vocal folds. It is governed by a single time constant which can be identified with the physiological parameter phonation onset time. This parameter reflects the laryngeal state prior to phonation and can be used as a quantitative classification criterion in order to assess the phonation onset in clinical diagnosis. The extraction of the phonation onset time from simulated time series using a simplified two-mass model and from digital high-speed videos is described in detail. It shows a good agreement between theory and measurement.

Direct evaluation of high-speed recordings of vocal fold vibrations.

A digital high-speed camera system for the endoscopic examination of the larynx allows recording speeds of up to 5,600 frames/s. Recordings of up to 1 s duration can be stored and used for further evaluation. Combined with an image processing program the system is able to render x-t diagrams of vocal cord movement. Data acquired from different locations of each vocal cord can be plotted separately. All of the known objective parameters of the voice can be derived from highspeed glottograms.

Mutant of rhodopseudomonas spheroides unable to grow aerobically.

We isolated a mutant of Rhodopseudomonas spheroides that grows normally under photosynthetic conditions but is unable to grow exponentially under aerobic conditions. Photosynthetically grown cultures of the mutant increase in mass and cell number for about 10 hr after transfer to aerobic conditions. During this time, no heme pigments are synthesized and the Q(O(2)) declines. In the mutant, synthesis of heme pigments is obligatorily coupled to synthesis of bacteriochlorophyll.