Histopathological Image Analysis: A Review <s> VII. LOOKING AHEAD: FUTURE TRENDS, OPEN PROBLEMS <s> A computerized method to automatically detect malignant masses on digital mammograms based on bilateral subtraction to identify asymmetries between left and right breast images was developed. After the digitization, in order to align left and right mammograms the breast border and nipple were automatically detected. Images were corrected to avoid differences in brightness due to the recording procedure. Left and right mammograms were subtracted and a threshold was applied to obtain a binary image with the information of suspicious areas. The suspicious regions or asymmetries were delimited by a region growing algorithm. Size and eccentricity tests were used to eliminate false-positive responses and texture features were extracted from suspicious regions to reject normal tissue regions. The scheme, tested in 70 pairs of digital mammograms, achieved a true-positive rate of 71% with an average number of 0.67 false positives per image. Computerized detection was evaluated by using free-response operating characteristic analysis (FROC). An area under the AFROC (A1) of 0.667 was obtained. Our results show that the scheme may be helpful to the radiologists by serving as a second reader in mammographic screening. The low number of false positives indicates that our scheme would not confuse the radiologist by suggesting normal regions as suspicious. <s> BIB001 </s> Histopathological Image Analysis: A Review <s> VII. LOOKING AHEAD: FUTURE TRENDS, OPEN PROBLEMS <s> Abstract Rationale and Objectives The authors evaluated the effect of optimal neural network architecture selection on the performance of a computer-aided diagnostic system designed to detect microcalcification clusters on digitized mammograms. Materials and Methods The authors developed a computer program to detect microcalcification clusters automatically on digitized mammograms. Previously, they found that a properly selected and trained convolution neural network (CNN) could reduce false-positive (FP) findings and therefore improve the accuracy of microcalcification detection. In the current study, they evaluated the effectiveness of the CNN optimized with an automated optimization technique in improving the accuracy of the microcalcification detection program, comparing it with the manually selected CNN. An independent test data set was used, which included 472 mammograms selected from the University of South Florida public database and contained 253 biopsy-proved malignant clusters. Results At an FP rate of 0.7 cluster per image, the film-based sensitivity was 84.6% for the optimized CNN, compared with 77.2% for the manually selected CNN. For clusters imaged on both craniocaudal and mediolateral oblique views, a cluster could be considered detected when it was detected on one or both views. For this case-based approach, at an FP rate of 0.7 per image, the sensitivity was 93.3% for the optimized and 87.0% for the manually selected CNN. Conclusion The classification of true and false signals is an important step in the microcalcification detection program. An optimized CNN can effectively reduce FP findings and improve the accuracy of the computer-aided detection system. <s> BIB002 </s> Histopathological Image Analysis: A Review <s> VII. LOOKING AHEAD: FUTURE TRENDS, OPEN PROBLEMS <s> This paper presents a generalizable architecture for a grid-enabled biomedical imaging application, used for processing of pathological images for computer-aided prognosis. The presented architecture provides the scientists and developers a collaborative environment for management of remote image data and algorithm repositories and job execution over the grid. It is specifically designed for processing large-scale images in a distributed environment. In addition to the architecture, we also present its application to the computer-aided neuroblastoma prognosis as well as performance evaluation of the developed system using digitized pathological images. <s> BIB003 </s> Histopathological Image Analysis: A Review <s> VII. LOOKING AHEAD: FUTURE TRENDS, OPEN PROBLEMS <s> Neuroblastoma is one of the most malignant childhood cancers affecting infants mostly. The current prognosis is based on microscopic examination of slides by expert pathologists, a process that is error-prone, time consuming and may lead to inter- and intra-reader variations. Therefore, we are developing a Computer Aided Prognosis (CAP) system which provides computerized image analysis to assist pathologist in their prognosis. Since this system operates on relatively large- scale images and requires sophisticated algorithms, it takes a long time to process whole-slide images. In this paper, we propose a novel and efficient approach for the execution of a CAP system for neuroblastoma prognosis, using the graphics processing unit (GPU). By leveraging high memory bandwidth and strong floating point operation capabilities of the GPU, our goal is to achieve order of magnitude reduction in the overall execution time as compared to that on a CPU alone. The proposed approach was tested on a set of testing images with a promising accuracy of 99.4% and an execution performance gain factor up to 45 times compared to C++ code running on the CPU. <s> BIB004
Since histopathological image analysis is inherently a crossdisciplinary area, there are unique challenges to the dissemination of research results. One of these is the wide range of publications in which research is published. While there are a few journals that focus on the automated analysis of medical imagery, the majority of histopathological image analysis tends to be published in the leading journals of the researchers' field (pathology, computer vision, etc.). Additionally, there is a need for more evidence regarding the clinical applicability and importance of automated histopathology image analysis methods. We have mentioned areas throughout this paper for which we anticipate ongoing research to have a clear and tangible affect on clinical and pathology workflow. Comparison of the various methods presented in the literature is difficult, since each research team uses their own dataset and presents their results with different metrics. There is a great need for standard datasets and ground truth for validation of methods. As an example, researchers at the University of South Florida have put together a database of digital mammograms. BIB001 While the variety of conditions studied in histopathology image analysis is greater, it is still important that standard datasets be compiled as well as a standard metric of performance. This will allow for direct comparison of the variety of analysis methods being reported in the literature. An additional complication is the variety of analyses performed on the histopathology imagery. Thus, there is a need for a dataset with ground truth pertaining to all the analyses described in this paper. Going forward, clinical annotation of histopathology data will be a large bottleneck in the evaluation of histopathology related CAD algorithms. Apart from the time constraints on the pathologist to generate this data, the process should be streamlined with active communication between the image analysis scientists and the clinicians with regard to the sort of annotation required, the format and scale at which the annotation is generated, and the ease with which the data can be shared (since histopathology files typically tend to be very large). For instance, the sophistication of annotation required to train a CAD system to distinguish cancerous versus noncancerous regions on pathology images may be very different than the annotation detail required to train a classifier to distinguish grades of cancer. While for the former problem the annotation could be done on a coarser scale (lower resolution), the latter annotation may require explicit segmentation of glands and nuclei, a far more laborious and time consuming process. Due to the large size of pathological images, usually it is not possible to process the whole image on a single-core processor. Therefore, the whole image may be divided into tiles and each tile is processed independently. As a consequence, automatic load balancing in the distribution of the cases to different processors need to be handled carefully BIB003 . Additionally, the processing can be accelerated even further by the use of graphical processing units (GPUs), cell blades, or any other emerging high-performance architecture BIB004 . Histopathological image analysis system evaluation needs to be carried out in a statistical framework. Depending on whether it is a problem of detection (e.g., nuclei detection) or characterization (e.g., grading), some commonly accepted evaluation methodologies need to be followed. Some of these methods, e.g., receiver operating characteristics (ROC) and free response operating characteristics (FROC), have been successfully used for many years in radiology BIB002 . These techniques could be adopted or adapted accordingly. The level and detailed of quantitative evaluation will vary as a function of the specific problem being addressed. For instance, in order to evaluate a nuclear segmentation algorithm on a digitized histological section containing several tens of thousands of nuclei, it is unreasonable to expect that a human reader will be able to manually annotate all nuclei. Evaluation of the scheme may have to be performed on randomly chosen segments of the image. Similarly, if the ultimate objective of the CAD algorithm is, for instance, cancer grading, perfect segmentation of histological structures may not guarantee perfect grade-based classification. Evaluation should hence be tailored towards the ultimate objective that the CAD algorithm is being employed for. Additionally, special attention needs to be paid to clearly separate training and testing datasets and explain the evaluation methodology.