Characterization of cancer diseases and preparation of diagnostic reports after analyzing tissue specimens and several cell samples are provided by pathologists. One of the most successful strategies in pathology is to divide tumors into different subtypes and to adapt the treatment for each tumor. However, this approach has put a big burden on pathologists, who are reviewing tissue samples under the light of the microscope. Because, tumors have about 200 subtypes and pathologies are facing a growing demand for accurate and fast diagnosis and also patient safety. Therefore, digital pathology has been important and growing rapidly. Advances in computer technology such as computing power, faster networks and cheaper storage have enabled pathologists to manage images more easily than in the last decade. Novel pathology tools have a potential for automated and faster diagnosis and also better management of data. Moreover, it enables re-reducibility, validation of results, quality assurance and sharing of new ideas at anywhere and anytime. Advances in digital pathology have been reviewed in this paper. It seems that innovations in technologies will not only provide important improvements in pathology service, but also they will change healthcare and research fundamentally despite some challenges. Keywords: Cell detection, computer assisted diagnosis, digital pathology, image analysis, nuclei segmentation, tissue classification.
Anahtar KelimelerDoku bölütlemesi, Kafa MR görüntüsü, Kafatası bölütlemesi, Kararsal yaklaşım, Olasılıksal yaklaşım, Lezyon bölütlemesi Özet: Nörodegeneratif hastalıkların teşhisinde veya tedavi sürecinde beyin dokularındaki değişim, kapladığı alan, oluşmuş ise lezyonların sayısı, konumu ve büyüklüğü gibi bilgilere ihtiyaç duyulmaktadır. Bu amaçla, kafatası, beyin dokuları ve lezyonlar tıbbi görüntülerden elcil yöntemle bölütlenmekte; bu yapıların kenarlarına, lezyonların sayı ve büyüklük değerlerine kişisel olarak karar verilmektedir. Görüntülerin görsel olarak incelenip analiz edilmesi, doktorlar için zaman alıcı, yorucu ve dikkat gerektiren bir işlemdir. Bununla birlikte, görüntüleme tekniğinden kaynaklanan gürültü ve görüntüdeki gri seviye değişimlerinin düşük olması, bu görsel analizi ve elcil yöntemle görüntü bölütlemeyi daha da zorlaştırmaktadır. Bu durum, kişisel değerlendirme sonuçlarını etkilemekte, farklı doktorların aynı görüntüde farklı kararlar vermesine, hatta aynı doktorun aynı görüntü üzerinde, farklı zamanlarda farklı kararlar vermesine sebep olabilmektedir. Bu nedenle, bu çalışmada, kafa görüntülerinden kafatası, beyin dokusu ve lezyon bölütlemesini otomatik olarak gerçekleştiren bir bilgisayar destekli yaklaşım önerilmektedir. Önerilen bütünleşik yaklaşımda, manyetik rezonans görüntüleri kullanılmış olup, kafatası ve doku bölütlemesi Gauss Karma Modele dayalı olarak olasılıksal bir yöntem ile sağlanırken, lezyon bölütlemesi düzey kümesine dayalı kararsal bir yöntem ile gerçekleştirilmiştir. Geliştirilen yazılım sayesinde, lezyon bölütleme başarıyla (maksimum simetrik yüzey uzaklığı 5.76±3.24 mm) gerçekleştirilebilmektedir. Abstract: Information such as changes in brain tissues, their area, number of lesions if they exit, size of lesions etc. is required for diagnosis of neurodegenerative diseases and also during treatment of these diseases. For this reason, skull, brain tissues and lesions are segmented manually from medical images; edges of these structures, number and size of lesions are determined subjectively. Visual examination and analysis of images is a time consuming and tedious task. Also, noise caused by imaging, and low contrast in the images make much more difficult the visual analysis and manual segmentation. This case, affects subjective evaluations, causes different decisions of different doctors on the same image, even different decisions of the same doctor on the same image at different times. Therefore, in this work, a computer aided approach that achieves automated segmentation of skull, brain tissues and lesions is proposed. In the proposed hybrid approach, magnetic resonance images have been used, skull and tissue segmentation has been performed by a probabilistic method based on Gaussian Mixture Model, while lesion segmentation has been performed by a deterministic method based on level set technique. By the developed software, lesion segmentation can be performed successfully (maximum symmetric surface distance is 5.76±3.24 mm). Automated Segmentation of Skull, Tissue a...
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